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X-ray images typically contain complex background information and abundant small objects, posing significant challenges for object detection in security tasks. Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas. [ABSTRACT FROM AUTHOR]

This study presents a novel method for the nighttime detection of waterborne individuals using an enhanced YOLOv5s algorithm tailored for infrared thermal imaging. To address the unique challenges of nighttime water rescue operations, we have constructed a specialized dataset comprising 5736 thermal images collected from diverse aquatic environments. This dataset was further expanded through synthetic image generation using CycleGAN and a newly developed color gamut transformation technique, which significantly improves the data variance and model training effectiveness. Furthermore, we integrated the Convolutional Block Attention Module (CBAM) at the end of the last encoder's feedforward network. This integration maximizes the utilization of channel and spatial information to capture more intricate details in the feature maps. To decrease the computational demands of the network while maintaining model accuracy, Ghost convolution was employed, thereby boosting the inference speed as much as possible. Additionally, we applied hyperparameter evolution to refine the training parameters. The improved algorithm achieved an average detection accuracy of 85.49% on our proprietary dataset, significantly outperforming its predecessor, with a prediction speed of 23.51 FPS. The experimental outcomes demonstrate the proposed solution's high recognition capabilities and robustness, fulfilling the demands of intelligent lifesaving missions. [ABSTRACT FROM AUTHOR]

The navigation safety of polar ships depends on accurate monitoring of ice conditions along the route. Complex polar environments but scarce ice datasets pose challenges for deep learning‐based object detection methods. To address this issue, we propose an ice monitoring method based on polar environment adaptive image enhancement. This approach adaptively augments the ice dataset by simulating polar environments such as snowflakes and aurora to improve the training precision of ice detection models in harsh polar environments and meet the needs of safe navigation of polar ships. First, an image augmentation method based on randomly generated snowflake textures is proposed to address the difficulty of covering polar storm scenarios in existing ice datasets, which leads to weak generalization of ice monitoring models. Second, targeting the problem that the boundary between ice and background is confused by aurora phenomena, which severely reduces the ice monitoring accuracy, we propose an image augmentation method based on aurora simulations to improve the ice monitoring performance of trained models under aurora. Finally, an adaptive image augmentation strategy based on the brightness‐contrast threshold is proposed to optimize the snowflake density and aurora intensity in the augmented images to improve the discrimination of ice boundaries. Experimental results demonstrate the effectiveness of the proposed method, which improves the ice state detection accuracy from 0.640 to 0.950, thus providing a reliable guarantee for the safe navigation of polar ships. [ABSTRACT FROM AUTHOR]

To mitigate problems concerning small-sized spots on apple leaves and the difficulties associated with the accurate detection of spot targets exacerbated by the complex backgrounds of orchards, this research used alternaria leaf spots, rust, brown spots, gray spots, and frog eye leaf spots on apple leaves as the research object and proposed the use of a high-accuracy detection model YOLOv5-Res (YOLOv5-Resblock) and lightweight detection model YOLOv5-Res4 (YOLOv5-Resblock-C4). Firstly, a multiscale feature extraction module, ResBlock (residual block), was designed by combining the Inception multi-branch structure and ResNet residual idea. Secondly, a lightweight feature fusion module C4 (CSP Bottleneck with four convolutions) was designed to reduce the number of model parameters while improving the detection ability of small targets. Finally, a parameter-streamlining strategy based on an optimized model architecture was proposed. The experimental results show that the performance of the YOLOv5-Res model and YOLOv5-Res4 model is significantly improved, with the mAP0.5 values increasing by 2.8% and 2.2% compared to the YOLOv5s model and YOLOv5n model, respectively. The sizes of the YOLOv5-Res model and YOLOv5-Res4 model are only 10.8 MB and 2.4 MB, and the model parameter counts are reduced by 22% and 38.3% compared to the YOLOv5s model and YOLOv5n model. [ABSTRACT FROM AUTHOR]

Computer-vision-based plant leaf segmentation technology is of great significance for plant classification, monitoring of plant growth, precision agriculture, and other scientific research. In this paper, the YOLOv8-seg model was used for the automated segmentation of individual leaves in images. In order to improve the segmentation performance, we further introduced a Ghost module and a Bidirectional Feature Pyramid Network (BiFPN) module into the standard Yolov8 model and proposed two modified versions. The Ghost module can generate several intrinsic feature maps with cheap transformation operations, and the BiFPN module can fuse multi-scale features to improve the segmentation performance of small leaves. The experiment results show that Yolov8 performs well in the leaf segmentation task, and the Ghost module and BiFPN module can further improve the performance. Our proposed approach achieves a 86.4% leaf segmentation score (best Dice) over all five test datasets of the Computer Vision Problems in Plant Phenotyping (CVPPP) Leaf Segmentation Challenge, outperforming other reported approaches. [ABSTRACT FROM AUTHOR]

Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment. To precisely recognize these boundaries, a detection method for unmanned tractor plow areas based on RGB-Depth (RGB-D) cameras was proposed, and the feasibility of the detection method was analyzed. This method applied advanced computer vision technology to the field of agricultural automation. Adopting and improving the YOLOv5-seg object segmentation algorithm, first, the Convolutional Block Attention Module (CBAM) was integrated into Concentrated-Comprehensive Convolution Block (C3) to form C3CBAM, thereby enhancing the ability of the network to extract features from plow areas. The GhostConv module was also utilized to reduce parameter and computational complexity. Second, using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model, the mask image was processed to extract contour boundaries, align the contours with the depth map, and obtain the boundary distance information of the plowed area. Last, based on farmland information, the calculated average boundary distance was corrected, further improving the accuracy of the distance measurements. The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99% for plowed areas and that the ranging accuracy improved with decreasing detection distance. The ranging error at 5.5 m was approximately 0.056 m, and the average detection time per frame is 29 ms, which can meet the real-time operational requirements. The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units. [ABSTRACT FROM AUTHOR]

The increasing rate of minimally invasive procedures and the growing prevalence of cardiovascular disease have led to a demand for higher-quality guidance systems for catheter tracking. Traditional methods for catheter tracking, such as detection based on single points and applying masking techniques, have been limited in their ability to provide accurate pose information. In this paper, we propose a novel deep learning-based method for catheter tracking and pose detection. Our method uses a Yolov5 bounding box neural network with postprocessing to perform landmark detection in four regions of the catheter: the tip, radio-opaque marker, bend, and entry point. This allows us to track the catheter's position and orientation in real time, without the need for additional masking or segmentation techniques. We evaluated our method on a dataset of fluoroscopic images from two distinct datasets and achieved state-of-the-art results in terms of accuracy and robustness. Our model was able to detect all four landmark features (tip, marker, bend, and entry) used to generate a pose for a catheter with 0.285 ± 0.143 mm, 0.261 ± 0.138 mm, 0.424 ± 0.361 mm, and 0.235 ± 0.085 mm accuracy. We believe that our method has the potential to significantly improve the accuracy and efficiency of catheter tracking in medical procedures that utilize bi-plane fluoroscopy guidance. [ABSTRACT FROM AUTHOR]

The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models' performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery. [ABSTRACT FROM AUTHOR]

The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods could achieve superior performance in the segmentation task of the DeepFish dataset. We also expanded the labeling of specific fish species classification tags on the basis of the original semantic segmentation dataset of DeepFish and completed the multi-class instance segmentation task of fish based on the newly labeled tags. Based on the above two achievements, we propose a general and flexible self-iterative fish identification and segmentation standard workflow that can effectively improve the efficiency of fish surveys. [ABSTRACT FROM AUTHOR]

A route positioning system is a technology that identifies the current route when driving from one stop to the next, commonly found in public transportation systems such as shuttle buses that follow fixed routes. This is especially useful for smaller-scale services, such as shuttle buses, where using expensive technology and sensors for location tracking might not be feasible. Particularly in urban areas with tall buildings or mountainous regions with lots of trees, relying solely on GPS can lead to many errors. Therefore, this paper suggests a cost-effective solution that uses just one camera sensor to accurately determine the location of small-scale transportation services on fixed routes. For this, this paper uses a single-stage detection network that quickly identifies objects and tracks them using a simple algorithm. These detected features are compiled into a "codebook" using the bag-of-visual-words technique. During actual trips, this pre-created codebook is compared with landmarks that the camera sees. This comparison helps to determine the route currently being traveled. To test the effectiveness of this approach, this paper used the route of a shuttle bus on the Gachon University campus, which is similar to a downtown area with tall buildings or a wooded mountainous area. The results showed that the shuttle bus's route was recognized with an accuracy of 0.60. Areas with distinct features were recognized with an accuracy of 0.99, while stops with simple, nondescript structures were recognized with an accuracy of 0.29. Additionally, applying the SORT algorithm to enhance performance slightly improved the accuracy from 0.60 to 0.61. This demonstrates that our proposed method can effectively perform location recognition using only cameras in small shuttle buses. [ABSTRACT FROM AUTHOR]

In this study, we extensively evaluated the viability of the state-of-the-art YOLOv8 architecture for object detection tasks, specifically tailored for smoke and wildfire identification with a focus on agricultural and environmental safety. All available versions of YOLOv8 were initially fine-tuned on a domain-specific dataset that included a variety of scenarios, crucial for comprehensive agricultural monitoring. The ‘large’ version (YOLOv8l) was selected for further hyperparameter tuning based on its performance metrics. This model underwent a detailed hyperparameter optimization using the One Factor At a Time (OFAT) methodology, concentrating on key parameters such as learning rate, batch size, weight decay, epochs, and optimizer. Insights from the OFAT study were used to define search spaces for a subsequent Random Search (RS). The final model derived from RS demonstrated significant improvements over the initial fine-tuned model, increasing overall precision by 1.39 %, recall by 1.48 %, F1-score by 1.44 %, mAP@0.50 by 0.70 %, and mAP@0.50:0.95 by 5.09 %. We validated the enhanced model's efficacy on a diverse set of real-world images, reflecting various agricultural settings, to confirm its robustness in detecting smoke and fire. These results underscore the model's reliability and effectiveness in scenarios critical to agricultural safety and environmental monitoring. This work, representing a significant advancement in the field of fire and smoke detection through machine learning, lays a strong foundation for future research and solutions aimed at safeguarding agricultural areas and natural environments. [ABSTRACT FROM AUTHOR]

This study has important scientific, cultural and economic contributions. From a scientific point of view, the decipherment of Gokturkish texts is of critical importance for research on Turkish culture, history and language. This study will enable historians and researchers to analyze these documents more quickly and effectively. Culturally, the reading of Gokturkish texts will help us gain a deeper understanding of Turkish culture and history. For linguists and cultural researchers, understanding these texts can offer new perspectives on the richness and cultural heritage of the past. From an economic point of view, this thesis argues that computer-assisted reading technology can contribute to a faster and more efficient reading and understanding of Gokturkish texts, making it easier to analyze the documents. This in turn frees up more time and resources for researchers and cultural experts, allowing them to focus on future work. [ABSTRACT FROM AUTHOR]

The advancement of unmanned aerial vehicles (UAVs) has drawn researchers to update object detection algorithms for better accuracy and computation performance. Previous works applying deep learning models for object detection applications required high graphics processing unit (GPU) computation power. Generally, object detection models suffer trade-off between accuracy and model size where the relationship is not always linear in deep learning models. Various factors such as architectural design, optimization techniques, and dataset characteristics can significantly influence the accuracy, model size, and computation cost in adopting object detection models for low-cost embedded devices. Hence, it is crucial to employ lightweight object detection models for real-time object identification for the solution to be sustainable. In this work, an improved feature extraction network is proposed by incorporating an efficient long-range aggregation network for vehicle detection (ELAN-VD) in the backbone layer. The architecture improvement in YOLOv7-tiny model is proposed to improve the accuracy of detecting small vehicles in the aerial image. Besides that, the image size output of the second and third prediction boxes is upscaled for better performance. This study showed that the proposed method yields a mean average precision (mAP) of 57.94%, which is higher than that of the conventional YOLOv7-tiny. In addition, the proposed model showed significant performance when compared to previous works, making it viable for application in low-cost embedded devices. [ABSTRACT FROM AUTHOR]

Flying-object detection has become an increasingly attractive avenue for research, particularly with the rising prevalence of unmanned aerial vehicle (UAV). Utilizing deep learning methods offers an effective means of detection with high accuracy. Meanwhile, the demand to implement deep learning models on embedded devices is growing, fueled by the requirement for capabilities that are both real-time and power efficient. FPGA have emerged as the optimal choice for its parallelism, flexibility and energy efficiency. In this paper, we propose an FPGA-based design for YOLOv4 network to address the problem of flying-object detection. Our proposed design explores and provides a suitable solution for overcoming the challenge of limited floating-point resources while maintaining the accuracy and obtain real-time performance and energy efficiency. We have generated an appropriate dataset of flying objects for implementing, training and fine-tuning the network parameters base on this dataset, and then changing some suitable components in the YOLO networks to fit for the deployment on FPGA. Our experiments in Xilinx ZCU104 development kit show that with our implementation, the accuracy is competitive with the original model running on CPU and GPU despite the process of format conversion and model quantization. In terms of speed, the FPGA implementation with the ZCU104 kit is inferior to the ultra high-end GPU, the RTX 2080Ti, but outperforms the GTX 1650. In terms of power consumption, the FPGA implementation is significantly lower than the GPU GTX 1650 about 3 times and about 7 times lower than RTX 2080Ti. In terms of energy efficiency, FPGA is completely superior to GPU with 2–3 times more efficient than the RTX 2080Ti and 3–4 times that of the GTX 1650. [ABSTRACT FROM AUTHOR]

The Sundarban constitute the world's largest continuous mangrove forest comprising of the natural habitat of the Royal Bengal Tiger. There is an urgent need for developing a system that would help in monitoring, identifying, and counting the number of tigers not only ensuring the protection of the tigers but also prioritizing the least human disturbance and intervention to the physically inaccessible areas. In this paper, a system is proposed for the identification and detection of Royal Bengal Tigers using drone image-capturing with an active GPS and thermal detection. In this proposed work, the drone with an active GPS will help in monitoring the location and thermal scan to track the tigers. The captured image of the tiger by the drone will be passed to a Deep Convolutional Neural Network (DCNN) using YOLO and Faster RCNN that will help in the detection of the uncommon features of each tiger hence identifying and counting the number of tigers in a region. [ABSTRACT FROM AUTHOR]

Pine Wilt Disease (PWD) is one of the most dangerous and destructive disease in the global forest ecosystems. Based on a dataset of pine wilt disease infected trees that we collected and produced, we developed new technology derived from YOLOv5s to promote the detection performance of the PWD infected trees in this work, in which attention mechanisms, random backgrounds and modifications of the loss functions are integrated. In our strategy, six different attention mechanisms, i.e., SE, CA, CBAM, ECA, SimAM and NAM, are added to improve the detection of YOLOv5s algorithm. These mechanisms are added by embedding in the previous layer of the spatial pyramid pooling-fast structure and replacing all C3 layers in the backbone, respectively. All attention mechanisms added in various ways improves the detection results of PWD infected pine trees. Among them, SE, CBAM and NAM attention mechanisms show the most significant improvements. Because all these three attention mechanisms can specifically enhance the ability of the model to focus on the critical feature for densely distributed or complex pine forests with red broad-leaved trees with diseased and withered pine trees. Five other loss functions are adopted to replace CIoU loss function in the original YOLOv5 networks to examine their interactions in the detection of PWD infected trees. Among the five replaced loss functions, SIoU and WIoU losses are sensitive to color changes in the target, allowing them to effectively capture the distinctions of diseased trees, thereby increasing detection precision. Also, we acquired a model trained by incorporating a 10 % ratio of random backgrounds into our original dataset. This training approach can improve the precision of recognition in different environments, thereby enhancing its generalization capability. Therefore, our new developed method can contribute important works to prevent and control of these diseases in real applications.

The paper introduces an original method for effective spatial data processing, particularly important for land administration and real estate governance. This approach integrates Unmanned Aerial Vehicle (UAV) data acquisition and processing with Artificial Intelligence (AI) and Geometric Transformation algorithms. The results reveal that: (1) while the separate applications of YOLO and Hough Transform algorithms achieve building detection rates up to 77% and 83%, respectively, (2) a novel methodology is proposed to combine spatial data and assess their quality of the detected buildings by comparing the generated building polygons with existing cadastral maps. The evaluation uses a polygon-based comparison approach, which computes metrics such as Precision, Recall, F1-Score, and Accuracy based on the spatial relationships between predicted and reference building contours, (3) the weighted model showed about 7 % improvement in accuracy compared to cadastral data. This innovative approach substantially improves spatial data processing, aiding in implementing principles for real estate good governance and offering a valuable asset for various land administration applications.

IntroductionIn response to the current mainstream deep learning detection methods with a large number of learned parameters and the complexity of apple leaf disease scenarios, the paper proposes a lightweight method and names it LCGSC-YOLO. This method is based on the LCNet(A Lightweight CPU Convolutional Neural Network) and GSConv(Group Shuffle Convolution) module modified YOLO(You Only Look Once) framework.MethodsFirstly, the lightweight LCNet is utilized to reconstruct the backbone network, with the purpose of reducing the number of parameters and computations of the model. Secondly, the GSConv module and the VOVGSCSP (Slim-neck by GSConv) module are introduced in the neck network, which makes it possible to minimize the number of model parameters and computations while guaranteeing the fusion capability among the different feature layers. Finally, coordinate attention is embedded in the tail of the backbone and after each VOVGSCSP module to improve the problem of detection accuracy degradation issue caused by model lightweighting.ResultsThe experimental results show the LCGSC-YOLO can achieve an excellent detection performance with mean average precision of 95.5% and detection speed of 53 frames per second (FPS) on the mixed datasets of Plant Pathology 2021 (FGVC8) and AppleLeaf9.DiscussionThe number of parameters and Floating Point Operations (FLOPs) of the LCGSC-YOLO are much less thanother related comparative experimental algorithms.

Feeding behavior is known to affect the welfare and fattening efficiency of yaks in feedlots. With the advancement of machine vision and sensor technologies, the monitoring of animal behavior is progressively shifting from manual observation towards automated and stress-free methodologies. In this study, a real-time detection model for individual yak feeding and picking behavior was developed using YOLO series model and StrongSORT tracking model. In this study, we used videos collected from 11 yaks raised in two pens to train the yak face classification with YOLO series models and tracked their individual behavior using the StrongSORT tracking model. The yak behavior patterns detected in trough range were defined as feeding and picking, and the overall detection performance of these two behavior patterns was described using indicators such as accuracy, precision, recall, and F1-score. The improved YOLOv8 and Strongsort model achieved the best performance, with detection accuracy, precision, recall, and F1-score of 98.76%, 98.77%, 98.68%, and 98.72%, respectively. Yaks which have similar facial features have a chance of being confused with one another. A few yaks were misidentified because their faces were obscured by another yak’s head or staff. The results showed that individual yak feeding behaviors can be accurately detected in real-time using the YOLO series and StrongSORT models, and this approach has the potential to be used for longer-term yak feeding monitoring. In the future, a dataset of yaks in various cultivate environments, group sizes, and lighting conditions will be included. Furthermore, the relationship between feeding time and yak weight gain will be investigated in order to predict livestock weight.

Detecting people carrying firearms in outdoor or indoor scenes usually identifies (or avoids) potentially dangerous situations. Nevertheless, the automatic detection of these weapons can be greatly affected by the scene conditions. Commonly, in real scenes these firearms can be seen from different perspectives. They also may have different real and apparent sizes. Moreover, the images containing these targets are usually cluttered, and firearms can appear as partially occluded. It is also common that the images can be affected by several types of distortions such as impulse noise, image darkening or blurring. All these perceived variabilities could significantly degrade the accuracy of firearm detection. Current deep detection networks offer good classification accuracy, with high efficiency and under constrained computational resources. However, the influence of practical conditions in which the objects are to be detected has not sufficiently been analyzed. Our article describes an experimental study on how a set of selected image distortions quantitatively degrade the detection performance on test images when the detection networks have only been trained with images that do not present the alterations. The analyzed test image distortions include impulse noise, blurring (or defocus), image darkening, image shrinking and occlusions. In order to quantify the impact of each individual distortion on the firearm detection problem, we have used a standard YOLOv5 network. Our experimental results have shown that the increased addition of impulse salt-and-pepper noise is by far the distortion that affects the most the performance of the detection network.

BackgroundThe aim of this study was to develop and evaluate an automated approach for segmenting bone loss (BL) on periapical (PA) radiographs and predicting the stage and grade of periodontitis.MethodsOne thousand PA radiographs obtained from 572 patients were utilized for training while a separate set of 1,582 images from 210 patients were used for testing. BL was segmented using a U-Net model, which was trained with augmented datasets to enhance generalizability. Apex detection was performed using YOLO-v9, focusing on identifying apexes of teeth to measure root length. Root length was calculated as the distance between the coordinates of detected apexes and center of cemento-enamel junction (CEJ), which was segmented utilizing a U-Net algorithm. BL percentage (ratio of BL to the root length) was used to predict the stage and grade of periodontitis. Evaluation metrics including accuracy, precision, recall, F1-score, Intersection over Union (IoU), mean absolute error (MAE), intraclass correlation coefficients (ICC), and root mean square error (RMSE) were used to evaluate the models’ performance.ResultsThe U-Net model achieved high accuracy in segmenting BL with 94.9%, 92.9%, and 95.62% on training, validation, and test datasets, respectively. The YOLO-v9 model exhibited a mean Average Precision (mAP) of 66.7% for apex detection, with a precision of 79.6% and recall of 62.4%. The BL percentage calculated from the segmented images and detected apexes demonstrated excellent agreement with clinical assessments, with ICC exceeding 0.94. Stage and grade prediction for periodontitis showed robust performance specifically for advanced stages (III/IV) and grades (C) with an F1-score of 0.945 and 0.83, respectively.ConclusionThe integration of U-Net and YOLO-v9 models for BL segmentation and apex detection on PA radiographs proved effective in enhancing the accuracy and reliability of periodontitis diagnosis and grading.

Cuneiform writing, an old art style, allows us to see into the past. Aside from Egyptian hieroglyphs, the cuneiform script is one of the oldest writing systems. It emerged in the second half of the fourth millennium BC. Most people believe that the Sumerians originally created it in southern Mesopotamia. Many historians place Hebrew's origins in antiquity. For example, we used the same approach to decipher the cuneiform languages; after learning how to decipher one old language, we would visit an archaeologist to learn how to decipher any other ancient language. We propose a deep-learning-based sign detector method to speed up this procedure to identify and group cuneiform tablet images according to Hebrew letter content. The Hebrew alphabet is notoriously difficult and costly to gather the training data needed for deep learning, which entails enclosing Hebrew characters in boxes. We solve this problem by using pre-existing transliterations and a sign-by-sign representation of the tablet's content in Latin characters. We recommend one of the supervised approaches because these do not include sign localization: We Find the transliteration signs in the tablet photographs by comparing them to their corresponding transliterations. Then, retrain the sign detector using these localized signs instead of utilizing annotations. Afterward, a more effective sign detector enhances the alignment quality. Consequently, this research aims to use the Yolov8 object identification pretraining model to identify Hebrew characters and categorize the cuneiform tablets. Images illustrating Hebrew passages have been culled from a Hebrew-language book. This book is known as the Old Testament, and it was organized into around 500 illustrations to aid in reading and pronouncing the characters. Another ancient document was recently discovered in Iraq, dating back to 500. It reached over a thousand photos after pre-processing and augmentation. The cuneiform digital library initiative (CDLI) website and the Iraqi Museum have compiled photographs of cuneiform tablets, with over a thousand photos available in each language.

The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight (Erwinia amylovora) is still one of the most dangerous diseases in fruit production, as it can spread epidemically and cause enormous economic damage. All measures are therefore aimed at preventing the spread of the pathogen in the orchard and containing an infection at an early stage [1–6]. Efficiency in plant disease control benefits from the development of a digital monitoring system if the spatial and temporal resolution of disease monitoring in orchards can be increased [7]. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards.Between 2021 and 2024, data was collected on nine dates under different weather conditions and with different cameras. The data source locations in Germany were the experimental orchard of the Julius Kühn Institute (JKI), Institute of Plant Protection in Fruit Crops and Viticulture in Dossenheim, the experimental greenhouse of the Julius Kühn Institute for Resistance Research and Stress Tolerance in Quedlinburg and the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden–Pillnitz. The RGB images were taken on different apple genotypes after artificial inoculation with Erwinia amylovora, including cultivars, wild species and progeny from breeding.The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 × 1280 pixels of fire blight infected shoots, flowers and leaves in different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were acquired and integrated into the ERWIAM dataset as a separate class. Each fire blight symptom was annotated with the Computer Vision Annotation Tool (CVAT [8]) using 2-point annotations (bounding boxes) and presented in YOLO 1.1 format (.txt files). The dataset contains a total of 1611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital systems for plant disease monitoring.

El principal motivo de este artículo fue la práctica de reconocimiento de objetos, utilizando la tecnología de Ultralytics, YOLOv8, aplicando el aprendizaje supervisado y otros métodos de machine learning. Se tomó en cuenta las definiciones de la detección de objetos y entrenamiento de modelos para clasificar los residuos sólidos para que estos sean después reciclados, para luego identificar manualmente cada clase de objeto con el etiquetador LabelImg, tomando en cuenta las posiciones de cada objeto en las imágenes. Se analizaron 1517 imágenes dándonos unos resultados excelentes y considerables.