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Aiming at the problems of higher computational complexity and larger memory requirements of current object detection algorithm, we designed and implemented an FPGA-based deep learning object detection system. We also designed the hardware accelerator corresponding to the YOLOv2-Tiny object detection algorithm, modeled the processing delay of each accelerator module, and describe the design of the convolution module. The experimental results show that it is 5.5x and 94.6x of performance and energy gains respectively when comparing with the software Darknet on an 8-core Xeon server, and 84.8x and 67.5x over the software version on the dual-core ARM cortex-A9 on Zynq. Also, the current design outperforms the previous work in performance.

Since the original YOLOv3 model takes up a large amount of storage space， more initialization dataset samples and parameters are required. A deep learning object detection compression model YOLOv3-ADS was proposed based on YOLOv3. The proposed model uses the methods of splicing and stacking for data enhancement of the fewer representative initial datasets. It introduces the DIoU loss function， and improves the accuracy of object detection. Finally， YOLOv3-ADS model was compressed by sparse training and pruning rate threshold setting， which reduces the number of redundant nodes， parameters and the required storage space. The experimental results show that， compared with the existing YOLOv3 model， the average accuracy of the proposed YOLOv3-ADS compression model is increased by about 30%， from 0.6418 to 0.8368， and the number of parameters is reduced by 96.6%， from the original 63.0MB to 2.2MB. At the same time， the storage space required by YOLOv3-ADS model is reduced by 96.5%， from 252MB to only 8.81MB. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) aims to recognise new objects without having training samples of them. The kernel of the traditional regression method was to project the visual features into the semantic space, without taking full advantage of the sample information contained in the visual features, meanwhile the training computation was large. This paper proposed to project prototype to visual feature space, which was referred to as inverse projection. It had a very efficient closed-form solution. Extensive experiments on two benchmark datasets show that the proposed ZSL method significantly outperforms the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology 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.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics 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.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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.)

Deep learning models are highly susceptible to adversarial samples, and even minuscule image perturbations that are not perceptible to the naked eye can disable well-trained deep learning models. Recent research indicates that these perturbations can exist in the physical world. This paper provides insight into physical adversarial attacks on deep learning object detection models, clarifying the concept of physical adversarial attack and outlining the general process of such attacks on object detection. According to the different attack tasks, a series of physical adversarial attack methods against object detection networks in recent years are reviewed from vehicle detection and pedestrian detection. Other attacks against target detection models, other attack tasks and other attack methods are briefly introduced. The current challenges of physical adversarial attack are discussed, the limitations of adversarial training are leaded out, and future development directions and application prospect are suggested. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

[Objective] The types of soil and water conservation measures and their configuration modes are complicated. Accurate identification and fine extraction of detailed configuration information of soil and water conservation measures are the basis for obtaining the factor values of soil and water conservation measures. Methods The information acquisition methods of soil and water conservation measures mainly include traditional field surveys, satellite remote sensing images? and UAV close-range photography. The identification and extraction methods mainly include visual interpretation? traditional machine learning? object-oriented classification methods? and deep learning models. By combing the research results of identification and extraction methods of soil and water conservation measures at home and abroad, the existing shortcomings are summarised and the research prospects are put forward. Results］ Tn semantic segmentation, future feature fusion and multimodal learning, weak supervision and semi-supervised learning, integrated learning and meta-learning can be applied to the extraction of soil and water conservation measures. -Conclusion］ At present, there are few reports on the results of identification and extraction of soil and water conservation tillage measures. However > tillage measures a re com mon in agricultural practice, and the research on identification and extraction of tillage measures should be strengthened in the future. Artificial intelligence combined with big data technology is the development direction of efficient and accurate identification and extraction of soil and water conservation measures in the future. Tt is necessary to further study the use of serni-supervised and weakly supervised learning methods. combined with multi-modal learning. small sample labels and other methods to obtain high-quality labeled sample data for soil and water conservation. Extraction of point and linear engineering measures； the combination of deep learning algorithms such as multimodal learning and instance segmentation methods with object-oriented classification methods is applied to the identification and extraction of soil and water conservation plant measures to improve the classification and extraction accuracy of different soil and water conservation plant measures. So as to improve the information extraction method of various soil and water conservation measures, and provide support for accurately obtaining the factor value of soil and water conservation measures and calculating the carbon sink capacity of soil and water conservation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

Object detection is currently a research hotspot in the field of computer vision. With the development of deep learning, object detection algorithms based on deep learning are increasingly applied and their performance is constantly improved. This paper summarizes the latest research progress of object detection methods based on deep learning by summarizing common problems encountered in the process of object detection and corresponding improvement methods. This paper focuses on two types of object detection algorithms based on deep learning. In addition, the latest improvement ideas of target detection algorithms are summarized from the aspects of attention mechanism, lightweight network, multiscale detection. Finally, in view of the current problems in the field of target detection, the future development trend is prospected. And the feasible solution is put forward in order to provide reference ideas and directions for the follow-up research work in this field. [ABSTRACT FROM AUTHOR]

With the development of deep learning, object detection has gradually replaced manual quality inspection as the primary direction for industrial quality control. However, in the complex texture of the tile surface, there are more lowvisibility small defects, and the interference from the complex textured background is serious. This results in a high false detection and false alarm rate using traditional object detection methods. To enhance the efficiency of defect detection, this paper proposed a defect detection approach on complex textured tile surfaces based on the joint attention mechanisms of channels and spatial. Firstly, to enhance the feature expression of small defects, it proposed a selective feature fusion method by modeling the relationship between deep and shallow feature channels. Secondly, it designed a joint channel and spatial attention module that selects key feature channels and suppresses texture regions through channel and spatial attention,enabling the model to focus on learning defect features and enhancing its ability to discriminate between defects and texture.Finally, it validated the approach on a dataset of complexly textured ceramic tile surface defects. The experimental results demonstrate that compared to the AFF and CBAM methods, the selective feature fusion method and channel ＆ spatial joint attention achieved improvements of 5.3AP and 6.32AP, respectively. In addition, this paper compared the overall approach with the existing tile detection method YOLOv5 and texture fabric defect detection method AFAM, showing that it outperformed these methods, with respective improvements of 1.32AP and 2.12AP. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Driven by deep learning, object detection methods have made great progress in recent years. However, under adverse weather conditions, such as foggy, dust, rain, snow and other weather conditions, the images will be degraded and blurred, which is difficult to extract effective features and cause problems to the later object detection. This paper summarizes the research of domestic and foreign scholars on object detection tasks in adverse weather in recent years, and analyzes it from three aspects: the establishment and use of relevant datasets, the research of image restoration algorithms for adverse weather conditions and their impact on object detection tasks, and the application of domain adaptive methods of transfer learning for object detection. Meanwhile, the paper also compares the experimental results of relevant methods, and puts forward the research focus in the future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture 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.)

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center 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.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Street vendors are an indispensable part of the urban social ecosystem, but due to a lack of comprehensive understanding, many cities have adopted simple eviction policies, resulting in the gradual marginalization and stigmatization of the street economy. The efficient governance of street vendors requires the comprehensive investigation of their business scale and spatial distribution information. However, traditional methods have limitations in terms of automatically surveying large-scale street vendor information, particularly spatial distribution. This paper proposes a method for the automatic investigation spatial distribution of street vendors based on street-view images and a deep-learning object recognition model. Street-view images were collected at fixed intervals according to the urban road network, and 1, 957 images containing one or more vendors were selected through human-machine interaction to establish street vendor label data. To achieve high recognition model accuracy, the category labels were subdivided into four categories: ground stalls, table stalls, tricycle stalls, and small truck stalls, based on the goods carriers used by street vendors. A deep neural-networkbased image object detection model based on YOLO v4 was constructed to identify street vendors in the streetview image library, with an average F1 value of 0.77 and an mAP of 0.67. The accuracy of the model was satisfactory for investigating the number and location of street vendors covering the main roads in the city and then applying a kernel density distribution model to evaluate the spatial distribution pattern of street vendors. Using street vendors in Guangzhou as a case study, the proposed automatic investigation model identified 26,119 street vendors from 3,339,062 street-view images. The results showed that the street vendors were distributed in a multicenter aggregation pattern in the central urban area, mainly concentrated in areas with high pedestrian traffic, such as subway stations and urban villages; their numbers increased as road grades decreases. Street vendors were mainly distributed in areas with medium rents. The proposed method is helpful for performing the efficient, low-cost, and city-scale mapping of street vendors; the results obtained provide suggestions for formulating and implementing spatial governance policies for the informal economy and further provide suggestions for improving and implementing spatial governance policies for open and diverse urban street-view images. The results can be used as a reference for the location preference analysis of practitioners, the exploration of NIMBY syndrome, and the determination of the formalization zone. Although street-view images have an insufficient spatiotemporal coverage, using them to perform street vendor investigations is a low-cost and efficient method compared with the use of traditional investigation methods and data sources. In addition, the method proposed in this article can be coupled with multitask deep learning algorithms to investigate additional dimensions of street vendor information, such as the sex, age, and type of business of street vendors. Relevant research needs to be conducted in the future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office 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.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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 With the aging population, the incidence of pressure injury (PI) is gradually increasing. This not only severely impacts the quality of life for patients but also increases healthcare expenditures. However, the early detection and accurate staging of PI heavily depend on specialized training. Objective To construct and validate an artificial intelligence model for the automatic detection and staging of PI aimed at enhancing the real-time nature, accuracy, and objectivity of PI diagnostics. Methods A total of 693 PI images from the electronic management system of pressure ulcers at Changshu No.1 People's Hospital were selected from January 2021 to February 2024, the images were randomly divided into a training set (551 images) and a test set (142 images), and categorized into six stages according to National Pressure Ulcer Advisory Panel (NPUAP) guidelines: StageⅠ (154 images), StageⅡ (188 images), StageⅢ (160 images), StageⅣ (82 images), deep tissue injury (57 images), and unstageable (52 images). A deep learning object detection model for PI was established using five different versions of the YOLOv8 [nano (n), small (s), medium (m), large (l) and extra large (x) ] neural network and transfer learning. The model evaluation metrics included accuracy, sensitivity, specificity, false positive rate, and detection speed. Finally, the model was deployed to a mobile application via the Ultralytics Hub platform, facilitating the application of the AI model in clinical practice. Results During the evaluation of a test set containing 142 PI images, the YOLOv8l version demonstrated high accuracy (0.827) and fast inference speed (68.49 fps), achieving the best balance between precision and speed among the YOLO versions. Specifically, it achieved an overall accuracy of 93.18% across all categories, a sensitivity of 76.52%, a specificity of 96.29%, and a false positive rate of 3.72%. Among the six stages of PI, the model achieved the highest accuracy for StageⅠat 95.97%. The accuracies for StageⅡ, StageⅢ, StageⅣ, deep tissue injury, and unstageable were 91.28%, 91.28%, 91.95%, 95.30%, and 93.29%, respectively. In terms of processing speed, YOLOv8l took a total of 2.07 seconds to process 142 images, averaging 68.49 PI images per second. Conclusion The AI model based on the YOLOv8l network can quickly and accurately detect and stage PI. Deploying this model to a mobile app allows for portable use in clinical practice, demonstrating significant potential for clinical application.

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research 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.)

Aiming at the automatic correction of the bubble level with large angle of inclination, the models in the field of deep learning object detection were studied, and a method based on deep learning object detection models was designed. The YOLO and SSD models were trained on the data set of bubble level images with different light conditions. By adopting K-means clustering algorithm, the effect of different anchor box numbers on YOLO model’s performance was analyzed. And two models’ detection accuracy and average IOU werecompared. Two edge fitting methods combining Progressive Probabilistic Hough transform with least square method were designed and two methods’ accuracy was compared. Aiming at this application, Client/Server network structure was used. The images were sent to the server and calculated. The calculation results were sent back to the client to control motor to correct the bubble level. The results indicate that the proposed method can detect the bubble level with large angle of inclination, and is more efficient than the conventional methods. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui 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.)

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To accomplish the task of aerial manipulator’s autonomous grasping, it is very important to recognize and locate the object. At present, most of the recognition algorithms of aerial manipulators use traditional feature extraction methods. In order to improve the accuracy and efficiency of object recognition and positioning, this paper designs a visual recognition and positioning algorithm based on YOLOv5 deep learning object detection algorithm and RGB‑D sensor. The algorithm can detect the object in real‑time and estimate its pose, which serves for the grasping task of aerial manipulator. At the same time, aiming at the problems that the deep learning algorithm has a huge amount of calculation and cannot achieve high‑performance real‑time detection in the embedded system, model quantization is introduced to optimize the algorithm, which can greatly improve the processing speed of the algorithm. This paper introduces the overall framework and implementation process of the algorithm and verifies the effectiveness of some algorithms of object detection and pose estimation by using the COCO dataset and motion capture system. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

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With the extensive deployment of deep learning technology in industry, automated systems applied in transportation, loading and unloading, packaging, sorting and other links have become a research hotspot in warehousing and logistics industry. Aiming at robot box unstacking scene, a point cloud center prediction-clustering network (CPCN) is proposed based on the deep learning method, which can segment the box stack and calculate the center coordinates of the upper surface of each box. Based on the traditional semantic-instance joint segmentation structure, CPCN designs a central prediction module and a central reinforcement module for the instance segmentation branch. The central prediction module avoids the error of central point segmentation by directly locating the instance center, and the central reinforcement module makes the points belonging to the same instance converge to the center in the feature space, both of which effectively enhance the identification ability of the instance features. In addition, the central-instance clustering method designed in the part of instance feature processing calculates the instance label by directly measuring the distance of the instance feature, which greatly reduces the computing time. Experiments on the box data set show that compared with the existing methods, the average accuracy of CPCN is improved by 0.7 percentage points at the lowest and 17.2 percentage points at the highest, the accuracy of instance center reaches 94.4%, the center offset is as low as 13.70 mm, and the reasoning speed is faster than that of the same type of joint division network. CPCN is more targeted for the box instance segmentation and has good application value. [ABSTRACT FROM AUTHOR]

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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

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