Search

In this study, a ratiometric fluorescent sensor CdTe QDs@ZIF-8 with butterfly spectra is successfully constructed by in situ encapsulating mercaptopropionic acid-modified CdTe quantum dots in zeolitic imidazolate framework-8 (ZIF-8) with a simple strategy, and used for the detection of tetracycline in fluorescence/smartphone colorimetry dual-mode. ZIF-8 not only reduces the agglomeration of the quantum dots but also surprisingly generates a new green fluorescence signal at 524 nm while the red fluorescence of the CdTe quantum dots at 650 nm quenches when tetracycline is added. The two opposing fluorescence signals create a butterfly-shaped fluorescence spectrum, allowing the sensor to detect tetracycline over a linear range of 0–70 μM with the detection limit (LOD) of 0.0155 μM by using a ratiometric fluorescence technique. What is more, based on the obvious color change of the fluorescent sensor gradually from red to green under UV light, a highly stable point-of-care testing sensor has been developed for on-site detection of tetracycline through color recognition by smartphones, which can be used for real-time detection of this antibiotic in the range of 0–1000 μM with the LOD of 0.0249 μM. This work provides a simple and efficient method for the on-site detection of tetracycline. [ABSTRACT FROM AUTHOR]

Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50, and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets. [ABSTRACT FROM AUTHOR]

The Japanese orange fly, Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae), is a univoltine tephritid pest of citrus fruits. Its occurrence in citrus-growing regions restricts the export of citrus fruits owing to plant quarantine regulations. We investigated whether the B. tsuneonis infestation rate could meet export quarantine standards if proper preharvest management were carried out. Our approach involved using orchards unsuitable for B. tsuneonis occurrence, implementing appropriate pesticide control, and removing suspected infested fruits. Sunny, open orchards away from any thickets that could serve as hiding places for the adults were chosen as the demonstration orchards. During the peak adult emergence and larval hatching periods, pesticides were sprayed to protect the fruits from infestation. We observed a total of 724,296 fruits throughout the fruit coloring period and removed 1027 suspected infested fruits. We dissected 58,193 harvested fruits and confirmed that they were not infested. No adults were captured during trap surveys in these orchards. In contrast, poorly managed orchards in the same area consistently exhibited fruit damage by B. tsuneonis larvae, and adults were captured in trap surveys. These results prove the feasibility of establishing damage-free orchards through appropriate preharvest management, even in regions where B. tsuneonis is distributed. [ABSTRACT FROM AUTHOR]

When applied to functional analysis results, structured visual inspection criteria have resulted in improvements in the levels of agreement between raters as well as earlier identification of the function of challenging behavior. However, multistep criteria can be difficult to apply in real time, which could be a barrier to widespread adoption in practice. This study evaluated a Microsoft‐Excel‐based functional analysis decision support system (FADSS), which could aid behavior analysts with interpreting functional analysis results. Final overall agreement between the FADSS and post hoc visual inspection was high at 95%. Final overall agreement between the post hoc results generated by FADSS and ongoing results generated by FADSS was acceptable at 81%, representing a 50% increase in efficiency. These results indicate that FADSS could aid behavior analysts when interpreting functional analysis results in real time. [ABSTRACT FROM AUTHOR]

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with "one-line knit to shape" capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry. [ABSTRACT FROM AUTHOR]

Objectives: Evaluating gingival thickness is an essential factor for esthetics, function, and the maintenance of soft tissue health around natural teeth and dental implants. This study aimed to evaluate the accuracy of visual inspection procedures used to identify gingival phenotype by clinicians with different expertise and level of experience. Method and materials: The gingival phenotypes of a total of 200 cases were identified as either thick or thin via visual assessment with clinical photographs by eight clinicians. One periodontist examined the gingival phenotype of all subjects based on the translucency of the periodontal probe through the gingival sulcus. The accuracy between these two methods was evaluated using percentile agreement and k statistics. Results: The mean percentage of accurately identified cases was 75% irrespective of the clinician's experience and expertise. Almost half of the thin phenotype cases were misclassified by clinicians. Inter-examiner agreement was generally fair to moderate and intra-examiner agreement was generally moderate. Conclusion: Within the limits of this study, the present findings demonstrated that the visual inspection method misclassified the cases, especially thin gingival phenotype, compared to the probe translucency method. Therefore, there is a need to identify a universally accepted, accurate method and clearclinical definitions. [ABSTRACT FROM AUTHOR]

Alkali-activated concrete (AAC) gained considerable attention from the beginning of its discovery as a substitute for conventional concrete (CC) due to its superior properties and powerful environmental impact. This paper investigated physical and mechanical characteristics, considering the presence of polypropylene fibers (PPFs), of ambient cured alkali-activated slag concrete (AASC) at ambient temperature, 25°C, and after exposing to elevated temperatures, 300°C, 600°C, and 900°C. For a comparison purpose, a similar CC was also investigated by observing changes in the physical and mechanical characteristics. Visual inspection, weight changes, compressive strength, flexural strength, bond strength, and ultrasonic pulse velocity tests were carried out. Also, scanning electron microscope (SEM) and thermogravimetric analysis (TGA) tests were used for microstructure analysis. Test results demonstrated that the AASC achieved higher mechanical properties than similar CC at ambient temperature. Results also showed that the AASC has a higher resistance to elevated temperatures than the CC due to its higher resistance to spalling and cracking, its higher recorded residual mechanical properties after exposure to elevated temperatures, and its higher thermal stability and resistance to degradation recorded by the microstructure analysis. The inclusion of polypropylene fibers decreased most of the studied properties for both AASC and CC. However, the reduction in mechanical properties for AASC was higher than that for CC at equivalent PPF content. [ABSTRACT FROM AUTHOR]

Developments in drones and imaging hardware technology have opened up countless possibilities for enhancing structural condition assessments and visual inspections. However, processing the inspection images requires considerable work hours, leading to delays in the assessment process. This study presents a semantic segmentation architecture that integrates vision transformers with Laplacian pyramid scaling networks, enabling rapid and accurate pixel-level damage detection. Unlike conventional methods that often lose critical details through resampling or cropping high-resolution images, our approach preserves essential inspection-related information such as microcracks and edges using non-uniform image rescaling networks. This innovation allows for detailed damage identification of high-resolution images while significantly reducing the computational demands. Our main contributions in this study are: (1) proposing two rescaling networks that together allow for processing high-resolution images while significantly reducing the computational demands; and (2) proposing Dmg2Former, a low-resolution segmentation network with a Swin Transformer backbone that leverages the saved computational resources to produce detailed visual inspection masks. We validate our method through a series of experiments on publicly available visual inspection datasets, addressing various tasks such as crack detection and material identification. Finally, we examine the computational efficiency of the adaptive rescalers in terms of multiply–accumulate operations and GPU-memory requirements. [ABSTRACT FROM AUTHOR]

Visual inspection tasks often require humans to cooperate with artificial intelligence (AI)‐based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate molds contained faulty bars or not, and were always informed whether the AI had classified the mold as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mold and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis. [ABSTRACT FROM AUTHOR]

AbstractThe increasing emphasis on the quality and safety of agricultural products, which are vital to global trade and consumer health, has driven the innovation of cost-effective, convenient, and rapid smart detection technologies. Smartphones, with their interdisciplinary functionalities, have become valuable tools in quantification and analysis research. Acting as portable, affordable, and user-friendly analytical devices, smartphones are equipped with high-resolution cameras, displays, memory, communication modules, sensors, and operating systems (Android or IOS), making them powerful, palm-sized remote computers. This review delves into how visual inspection technology and smartphones have enhanced the quality and safety of agricultural products over the past decade. It also evaluates the key features and limitations of existing smart rapid inspection methods for agricultural products and anticipates future advancements, offering insights into the application of smart rapid inspection technology in agriculture. [ABSTRACT FROM AUTHOR]

Traditional inspection methods often fall short in detecting defects or damage in fibre-reinforced polymer (FRP) composite structures, which can compromise their performance and safety over time. A prime example is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection of defects, enabling proactive maintenance and extending the lifespan of composite structures. This study explores the viability of using novel bio-inspired hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of laminated FRP composites exposed to transverse loading. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensors' efficacy in detecting BVID. Moreover, a comparison between the two transverse loading types, static indentation and low-velocity impact, is presented. The results suggest that integrating sensors into composite structures has a minimal effect on mechanical properties such as structural stiffness and energy absorption, while substantially improving damage visibility. Additionally, the influence of fibre orientation of the sensing layer on sensor performance is evaluated, and correlations between internal and surface damage are demonstrated. [ABSTRACT FROM AUTHOR]

Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the visual inspection of individual products or complex assemblies within highly variable production environments, utilizing next-generation VIS. These advanced VIS exhibit significant technical (hardware and software) enhancements, such as increased flexibility, reconfigurability, Computer Aided Design (CAD)-based integration, self-X capabilities, and autonomy, as well as economic improvements, including cost-effectiveness, non-invasiveness, and plug-and-produce capabilities. The new trends in VIS have the potential to revolutionize business models by enabling as-a-service approaches and facilitating a paradigm shift towards more sustainable manufacturing and human-centric practices. We extend the discussion to examine how these technological innovations, which reduce the need for extensive coding skills and lengthy reconfiguration activities for operators, can be implemented as a shared resource within a circular lifecycle. This analysis includes detailing the underlying business model that supports shared utilization among different stakeholders, promoting a circular economy in manufacturing by leveraging the capabilities of next-generation VIS. Such an approach not only enhances the sustainability of manufacturing processes but also democratizes access to state-of-the-art inspection technologies, thereby expanding the possibilities for autonomous manufacturing ecosystems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background Substandard and falsified (SF) medicines are a serious threat to public health in low- and middle-income countries (LMICs). Visual inspection of medicines and screening analysis using the Global Pharma Health Fund (GPHF)-Minilab are important in medicine quality surveillance in low-resource settings.Methods Recently, 260 medicine samples from Nigeria had been investigated for assay and dissolution according to the United States Pharmacopeia (USP). In the present study, these results were compared to the results of the investigation of the same samples by visual inspection and by GPHF-Minilab analysis by local personnel in Nigeria.Results Visual inspection identified many deficiencies of dosage units and packaging information in SF medicines. All four falsified medicines were readily identifiable, primarily from serious spelling errors in the labelling, and from manufacturer names which could not be verified using internet resources. In GPHF-Minilab disintegration testing, two samples did not disintegrate even after 60 min; both were found to fail USP dissolution testing with extreme deviations. Of the 20 samples which deviated in USP assay analysis by more than 20% from the declared API amount, seven (35%) were detected as non-compliant in TLC analysis. Evaluation by TLC image analysis with a recently developed smartphone application (named TLCyzer) increased sensitivity to 62.5% but led to an unacceptably low specificity (75.2%). Additional training of the local personnel improved the results of both TLC analysis and TLCyzer evaluation. Photographs of the visual deficiencies and of the TLC analysis results of the SF medicines are provided as PowerPoint and PDF slides with this publication, for future training courses of pharmacy staff and health workers in LMICs.Conclusion Visual inspection, and screening analysis with simple, rapid and inexpensive methods, are important in the surveillance for SF medicines in LMICs. This study provides data on the potential and the limitations of such screenings.

This study provides a comprehensive evaluation of three pavement surface treatments—Re-Ascon, Micro-surfacing, and Fog-seal—aimed at enhancing the longevity of asphalt concrete pavement. Utilizing visual inspection, bearing capacity measurements, and linear regression modeling, this study rigorously assesses the effectiveness of these strategies in real-world road environments. Notably, Re-Ascon emerges as the least damaged method in visual inspection, offering crucial insights into initial pavement conditions. Visual inspections consistently underscore Re-Ascon's outstanding performance in mitigating pavement distress, particularly in addressing cracking. Quantitative analysis using the Surface Curvature Index (SCI) from Falling Weight Deflectometer (FWD) measurements highlights Re-Ascon's superior elastic modulus (E1) and resistance to deflection compared to alternatives. Re-Ascon's 20 % SCI reduction compared to Fog-seal highlights its superior deflection resistance, supported by logarithmic elastic modulus values. Correlation analysis confirms Re-Ascon's resilience, maintaining low SCI even under higher loads and adverse weather conditions. Geographical variations and method-specific influences are revealed through PCI analysis, emphasizing the vital impact of preventive maintenance. The identified PCI threshold of 60 % guides timely interventions for major maintenance, while service life analysis forecasts approximately 3.0, 2.5, and 1.5 years for Micro-surfacing, Re-Ascon, and Fog-seal methods, respectively. In conclusion, this research furnishes critical insights into preventive maintenance methods, advocating a holistic assessment through visual, structural, and predictive perspectives, and emphasizing the need for ongoing research to advance sustainable strategies.

Moraxella catarrhalis (M. catarrhalis), once considered as a harmless commensal bacterium, is now recognized as a significant pathogen associated with human mucosal infection. Hence, development of rapid, accurate, and user-friendly detection method is crucial for effective control and management of M. catarrhalis infections. Here, we introduce the M. catarrhalis MCDA-FRT assay, a novel diagnostic approach based on multiple cross displacement amplification (MCDA) for the visual and fluorescent (FRT) detection of M. catarrhalis. This innovative method amplifies the target sequence through MCDA reaction and provides results through the visual observation of a fluorescent signal when exposed to blue light. The generation of the fluorescent signal is facilitated by the cleavage of the restriction endonuclease Nb.BsrDⅠ. The M. catarrhalis MCDA-FRT assay can efficiently detect M. catarrhalis at a temperature of 65 °C in 40 min. It exhibits a remarkable sensitivity of 35 fg of M. catarrhalis genomic DNA, with a specificity of 100%. Furthermore, the assay accurately identified all M. catarrhalis-infected clinical samples tested. In conclusion, the M. catarrhalis MCDA-FRT assay showcases outstanding performance in terms of simplicity, sensitivity, and specificity, which can be used as a promising diagnostic tool for the swift and reliable detection of M. catarrhalis in both field and basic medical settings.

In this study, a plastic infrared sorting machine was used to conduct sorting tests on the normal polyethylene terephthalate (PET) plastic bottles with blue, white, and green colors as well as the other plastic bottles made from polycarbonate (PC), [polyethylene (PE), and polypropylene under the dual-path excitation environment of visible and infrared light. Three common Retinex color enhancement algorithms were further utilize to identify the white -, blue-, and green-color PET bottles, and the recognition effect of plastic infrared sorting machines was investigated. The results indicated that there was no significant difference in the imaged images between PP, PC, and PET bottles with a white color under visible light. However, under infrared imaging, PP, PC, and PET bottles reflected green, purple, and blue light with significant differences. The HSV range value was expanded from 0-46 to 0-61, the background saturation was adjusted from 72 to 80, the missing proportion of white and blue PET bottles was reduced from 4. 58 % to 1. 26 %, and the misidentification rate of white bottles made from PP, PC, and PE was reduced from 3. 88 % to 2.69 %. There was no impact on the misidentification rate of the PE bottle, maintaining a recognition rate of 100 %. The best removal effect was obtained through the MSR color enhancement algorithm with removal rates of 94. 8 %, 95. 83 %, and 95. 83 % for the recognition of white, blue, and green PET bottles, respectively. [ABSTRACT FROM AUTHOR]

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%. [ABSTRACT FROM AUTHOR]

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called 'two-step cluster analysis' is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables. [ABSTRACT FROM AUTHOR]

Hull inspection is an important task to ensure sustainability of ships. To overcome the challenges of hull structure inspection in an underwater environment in an efficient way, an autonomous system for hull inspection has to be developed. In this paper, a new approach to underwater ship hull inspection is proposed. It aims at developing the basis for an end-to-end autonomous solution. The real-time aspect is an important part of this work, as it allows the operators and inspectors to receive feedback about the inspection as it happens. A reference mission plan is generated and adapted online based on the inspection findings. This is done through the processing of a multibeam forward looking sonar to estimate the pose of the hull relative to the drone. An inspection map is incrementally built in a novel way, incorporating uncertainty estimates to better represent the inspection state, quality, and observation confidence. The proposed methods are experimentally tested in real-time on real ships and demonstrate the applicability to quickly understand what has been done during the inspection. [ABSTRACT FROM AUTHOR]

Fire safety is an important consideration, particularly in buildings where there are significant risks linked to a possible fire breakout. Therefore, it is crucial to implement procedures and regulations in buildings to minimize fire damage. Despite the installation of various pieces of Fire Safety Equipment (FSE), over time their effectiveness may be reduced due to factors, such as failure, damage, and insufficient maintenance. For this reason, the fire safety inspection process came to ensure the FSE availability and efficiency. Visual fire safety inspection conducted by civil defense is found to be time-consuming and inefficient, primarily due to manual procedures and difficulty in identifying defects, leading to inaccurate results and low performance. The purpose of this research is to enhance and automate fire safety inspection by implementing deep learning and computer vision techniques in a mobile application, thus addressing the challenges associated with visual inspection. Weqaa application allows the inspector to point their mobile phone camera at the fire extinguisher, then determine the condition of the extinguisher, document it, and report it to the relevant authority to quickly determine the appropriate action procedure. Interviews with expert inspectors were performed to outline the required functions of the application. The mobile application was developed using Flutter and being integrated with the detection model to permit the user to inspect fire extinguishers. Initial testing of the application has exhibited promising results, with inspectors noting its competence in detecting violations and improving inspection processes. The use of the particular application enabled the inspectors to perform the required functions faster, more accurately, and with fewer errors compared to the visual inspection deployment, indicating the application's effectiveness in detecting violations. [ABSTRACT FROM AUTHOR]

Uncertainties and discrepant results in identifying crucial areas for emotional facial expression recognition may stem from the eye tracking data analysis methods used. Many studies employ parameters of analysis that predominantly prioritize the examination of the foveal vision angle, ignoring the potential influences of simultaneous parafoveal and peripheral information. To explore the possible underlying causes of these discrepancies, we investigated the role of the visual field aperture in emotional facial expression recognition with 163 volunteers randomly assigned to three groups: no visual restriction (NVR), parafoveal and foveal vision (PFFV), and foveal vision (FV). Employing eye tracking and gaze contingency, we collected visual inspection and judgment data over 30 frontal face images, equally distributed among five emotions. Raw eye tracking data underwent Eye Movements Metrics and Visualizations (EyeMMV) processing. Accordingly, the visual inspection time, number of fixations, and fixation duration increased with the visual field restriction. Nevertheless, the accuracy showed significant differences among the NVR/FV and PFFV/FV groups, despite there being no difference in NVR/PFFV. The findings underscore the impact of specific visual field areas on facial expression recognition, highlighting the importance of parafoveal vision. The results suggest that eye tracking data analysis methods should incorporate projection angles extending to at least the parafoveal level. [ABSTRACT FROM AUTHOR]

The number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are responsible for visual inspection of safety equipment and reporting defects. As the traditional approach of manually checking each piece of equipment can be time-consuming and inaccurate, this study aims to improve the inspection processes of safety equipment. Using computer vision and deep learning techniques, a detection model was trained to visually inspect fire extinguishers and identify defects. Fire extinguisher images were collected, annotated, and augmented to create a dataset of 7,633 images with 16,092 labeled instances. Then, experiments were carried out using YOLOv5, YOLOv7, YOLOv8, and RT-DETR. Pre-trained models were used for transfer learning. A comparative analysis was performed to evaluate these models in terms of accuracy, speed, and model size. The results of YOLOv5n, YOLOv7, YOLOv8n, YOLOv8m, and RT-DETR indicated satisfactory accuracy, ranging between 83.1% and 87.2%. YOLOv8n was chosen as the most suitable due to its fastest inference time of 2.7 ms, its highest mAP0.5 of 87.2%, and its compact model size, making it ideal for real-time mobile applications. [ABSTRACT FROM AUTHOR]

Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data. [ABSTRACT FROM AUTHOR]

Historical photographs play a significant role as records, documenting the various stages of the history of Egypt and reflecting our identity and social, cultural, and religious values. Silver gelatin prints were the dominant positive printing processes in the 20th century. One of the most common forms of damage found among photographic collections is tears, which may result from improper handling and misuse, inappropriate storage, disasters, and other factors. In the past, tears were mended using pressure-sensitive tapes by amateurs and conservators. Over time, these tapes caused damage to the photographic surfaces since they oxidised, forming stains that are difficult and sometimes impossible to remove. Recently, many gel-based systems have been developed for use in the conservation of different cultural and historical materials, particularly paper artifacts. The aim of this research is to evaluate the efficacy of Klucel G and agar, as gel systems that are favoured by conservation specialists, in removing pressure-sensitive tapes from the surfaces of silver gelatin prints, as well as to study their effect on the photographs themselves. Organic solvents were added to the gel systems, and invaluable old photographs were used for this study. After treatment, samples were subjected to artificial ageing at a temperature of 80°C and 65% RH. Evaluation of the selected treatments was carried out through visual inspection, microscopic examination, colorimetric measurements, and attenuated total reflectance Fourier transform infrared spectroscopy. Results showed that both gel-based systems with mixed organic solvents gave great results compared to the conventional methods in terms of tape removal. However, colour change was detected in the case of agar gel-treated samples. Agarose gel may be a more proper option. [ABSTRACT FROM AUTHOR]

Printed circuit board (PCB) inspection is as challenging issue in electronic manufacturing. Visual inspection is broadly used to perform this task and has been instigated by many researchers. This paper focuses on review of 2D vision-based methods in PCB inspection. Traditional and modern advances methods are explored, and their advantages and disadvantages are addressed. Moreover, current research gap through literature review investigation in this domain is addressed and potential solutions are described. Finally, direction for future studies is presented for further improvement of existing methods. [ABSTRACT FROM AUTHOR]

The surface of solar cell products is critically sensitive to existing defects, leading to the loss of efficiency. Finding any defects in the solar cell is a significantly important task in the quality control process. Automated visual inspection systems are widely used for defect detection and reject faulty products. Numerous methods are proposed to deal with defect detection and solar cell inspection. However, they suffer from high computation complexity and hardware requirement cost issues. To overcome these issues, in this study, an effective visual inspection method is proposed to detect different types of defects in solar cells. The proposed method is based on adapted morphological and edge detection algorithms. This method uses multiple uses of morphological and canny edge detection with adapted parameters to extract and highlight the objects on a solar cell. Then the detected objects are classified, and different defect types and components are detected using their features and classification algorithm. Experimental results indicate that the proposed method can meet the requirements for effectiveness and real-time processing and presents promising results compared to other existing algorithms. [ABSTRACT FROM AUTHOR]

Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50, and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets.

Dams are mega structures that retain huge volumes of water and their safety is important to national security, economy, and public interest. In this study, the types of regular measurements with their recorded value for Haditha Dam were checked for compliance with the standard international surveillance and monitoring procedures such as the procedures of the International Commission on Large Dams (ICOLD), the United States Bureau of Reclamation (USBR)and other to ensure safe dam operation and to avoid the scenario of the Derna Dam break, in Libya that occurred in September 2023. The result shows that the highest recorded settlement was found at station 41 on the left side of the dam body exceeding the design criteria (219 mm) by about 51 %, the frequency of measurement does not comply with the required international standards. There are 7 cells of pore pressure that exceeded the design criteria and the greatest percentage of exceedance was 21.57 % was found at pore pressure number 995. For piezometer reading, the percentage exceedance was 9.1% and it was found on the left side at piezometers number 58-8. The highest percentage of movement 62 % was found at join meter device number D18. The recorded seepage rate through the dam was found within the permitted limits. The visual inspection followed at Haditha Dam was not according to the standard required inspection.

The color of a product is one of the main factors that affect consumer choices. However, it can be difficult to discern the subtle differences in color in order to inspect or classify products without expensive and specialized equipment. In this study, we proposed an approach where standard off-the-shelf components (digital camera and RGB LED lights) can be used to robustly classify products based on their color. By varying the color of the illumination, environment-independent information of the color of the object can be obtained. Then, using a Log-Linearized Gaussian Mixture Neural Network (LLGMN), a neural network based on a statistical model, the objects can be effectively and robustly classified based on their color without requiring a large number of training data. An experiment was performed to verify the effectiveness of this approach. 8 samples with only a subtle difference in color were prepared, and a prototype was developed to be used to classify these 8 samples. We demonstrate that we are able to classify the 8 samples an accuracy of 100% when there was no ambient light and were still able to maintain an accuracy of 94.1% when there was ambient light in the room.

Objective: The purpose of this study was to assess how oral and maxillofacial surgeons used various diagnostic tools for oral cancer. Materials and Methods: A cross-sectional methodology was used, and a standardized questionnaire was given to oral and maxillofacial surgeons randomly chosen sample. The questionnaire gathered information on demographics and the use of diagnostic tools. Data analysis methods included Chi-square testing and descriptive statistics. Results: The study included 200 oral and maxillofacial surgeons in total. The most often used diagnostic tool (95%) was visual inspection, followed by toluidine blue staining (48%) and brush biopsy (32%). Less frequently used were newer methods like optical coherence tomography (12.5%) and autofluorescence imaging (15%). No significant correlations between demographic factors and patterns of use of diagnostic tools were found by Chi-square tests. Conclusion: The results show that oral and maxillofacial surgeons frequently use brush biopsy, toluidine blue staining, and ocular evaluation. However, there is a need for more widespread adoption of cutting-edge technologies. By removing obstacles and offering training opportunities, one can increase the use of diagnostic tools, improving patient outcomes and the diagnosis of oral cancer.

We implemented and evaluated our previous path planning method for inspection using unmanned aerial vehicles (UAVs) in real-world, and identified its shortcomings in handling positioning errors. Then, we proposed an enhanced method to address this problem. The previous method theoretically guaranteed complete coverage of targets and data quality. However, we verified it in bridge inspection experiments and found that the former has not been ensured. The crucial factors of data omission are clarified as the errors in UAV positioning. Our previous method relies on appropriately setting ideal allowances to counteract positioning errors, which is challenging in practice. Therefore, we proposed an enhanced path planning method, which adaptively adjusts allowances according to positioning error to prevent omission while minimizing waypoints. In the simulation including positioning disturbances, the enhanced method consistently achieved full coverage in 1000 times simulation with over 28% waypoints less than the previous one.

With the development of scientific information technology and the popularization of electronic devices, images and videos have become very important forms of information expression and carriers in our current lives. Accelerating the mining of valuable information content from massive data has become a very important aspect of current computer vision research. The saliency object detection method, which is related to human visual attention, is gradually being applied in computer processing. However, in current color depth models, the association mining of data depth clues is still far from sufficient, and there is still significant room for improvement in image quality. Based on this, an improved color depth detection model is proposed for information guided and multi feature fusion, and an absorption Markov model is introduced to optimize the guidance of low-level, middle-level, and high-level saliency maps, grasping different feature information contents. Subsequently, the gradual guidance of the network is achieved from aspects such as feature encoding, multi-scale and multi attention models, and attention refinement mechanisms. The experimental analysis of the fusion model proposed in the study showed that the average classification improvement accuracy of the fusion model reached 5.23%, and its error value was less than 0.1. The effectiveness on all four quantitative indicators exceeded 92%. The system’s detection response rate exceeded 93%, which is limited by the target object and results in a decrease in accuracy. This algorithm can provide reference value and means for target localization recognition and virtual scene detection.

Visible particles are a critical quality attribute for parenteral products and must be monitored. A carefully designed, executed, and controlled drug product manufacturing process including a final 100 % visual inspection and appropriate end-product controls ensures that visible particles are consistently minimized and demonstrates that the injectable DP is practically free from visible particles. Visual inspection, albeit appearing as a simple analytical procedure, requires several technical and operational controls to ensure adequate performance. To gather new data on particle visibility and shed light on this decade-old challenge, a multi-company blinded visual inspection threshold study was conducted. A major goal of the study was visual assessment of several particle types of different sizes in small volume vials, as a challenging configuration for visual inspection, across 9 biopharmaceutical companies in order to determine the visibility limit. The study results provide key insights into limitations and challenges of visual inspection, namely, no universal visibility limit can be applied to all particle types as the detectability varies with particle type, number, and size. The study findings underscore the necessity of setting realistic expectations on size-based visibility limits in visual inspection, robust procedures for analyst training and qualification, and harmonization of guidelines globally. [ABSTRACT FROM AUTHOR]

The inspection of power transmission and distribution systems is performed visually by foot patrolling and helicopter methods. These methods have various disadvantages such as time-consuming, high operating cost, safety issues, and improper results. To overcome this issue, power utility companies are searching for the alternatives like Unmanned Aerial Systems (UAS) or drones. UAS are safe, cost effective, and requires less time for power transmission line inspection compared to the regular methods. In this manuscript, a decisive flight path planning for UAS to visually inspect a power transmission line and towers is explained. The objective of this paper is to maximize the performance of three functions such as coverage of transmission tower, quality of captured image, and flight time. Second, proposing an automated inspection strategy for UAS to follow the overhead power transmission lines. These objectives are achieved by formulating a cost function, to convert the path planning into a safe operation for UAS. The results of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are compared to find the best path for UAS. The experimental finding shows that PSO algorithm has higher efficiency and effectiveness compared to the SA algorithm and benefits the UAS with a safe flight path for the inspection of power transmission towers. [ABSTRACT FROM AUTHOR]

Experimenters provided 33 graphical displays of hypothetical data depicted in a multielement experimental design to editorial board members of prominent, applied, behavior-analytic journals via an online survey. For each display, participants indicated (a) the presence or absence of experimental control and (b) the degree of experimental control (rated on a 1–100 scale). Each depiction varied systematically in (a) the number of data paths, (b) the number of data paths elevated above the control, (c) the mean difference between affected data paths and control conditions, and (d) the degree of variability within conditions. Correspondence among experts' ratings of experimental control was high across all presented graphical displays, supporting the reliability of visual analysis as an evaluative tool for these designs. [ABSTRACT FROM AUTHOR]