SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm
Abstract Vehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the d...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Computer Vision |
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| Online Access: | https://doi.org/10.1049/cvi2.12293 |
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| _version_ | 1846101844017807360 |
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| author | Shanshan Wang Bushi Liu Pengcheng Zhu Xianchun Meng Bolun Chen Wei Shao Liqing Chen |
| author_facet | Shanshan Wang Bushi Liu Pengcheng Zhu Xianchun Meng Bolun Chen Wei Shao Liqing Chen |
| author_sort | Shanshan Wang |
| collection | DOAJ |
| description | Abstract Vehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi‐scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability. |
| format | Article |
| id | doaj-art-60edc06f3b374d44bcfb2b164ef5db2f |
| institution | Kabale University |
| issn | 1751-9632 1751-9640 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj-art-60edc06f3b374d44bcfb2b164ef5db2f2024-12-28T11:01:56ZengWileyIET Computer Vision1751-96321751-96402024-12-011881149116110.1049/cvi2.12293SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithmShanshan Wang0Bushi Liu1Pengcheng Zhu2Xianchun Meng3Bolun Chen4Wei Shao5Liqing Chen6Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian ChinaFaculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian ChinaFaculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian ChinaFaculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian ChinaFaculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian ChinaShenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen Guangdong ChinaFaculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian ChinaAbstract Vehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi‐scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability.https://doi.org/10.1049/cvi2.12293computer visionobject detectionroad vehicles |
| spellingShingle | Shanshan Wang Bushi Liu Pengcheng Zhu Xianchun Meng Bolun Chen Wei Shao Liqing Chen SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm IET Computer Vision computer vision object detection road vehicles |
| title | SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm |
| title_full | SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm |
| title_fullStr | SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm |
| title_full_unstemmed | SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm |
| title_short | SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm |
| title_sort | sam y attention enhanced hazardous vehicle object detection algorithm |
| topic | computer vision object detection road vehicles |
| url | https://doi.org/10.1049/cvi2.12293 |
| work_keys_str_mv | AT shanshanwang samyattentionenhancedhazardousvehicleobjectdetectionalgorithm AT bushiliu samyattentionenhancedhazardousvehicleobjectdetectionalgorithm AT pengchengzhu samyattentionenhancedhazardousvehicleobjectdetectionalgorithm AT xianchunmeng samyattentionenhancedhazardousvehicleobjectdetectionalgorithm AT bolunchen samyattentionenhancedhazardousvehicleobjectdetectionalgorithm AT weishao samyattentionenhancedhazardousvehicleobjectdetectionalgorithm AT liqingchen samyattentionenhancedhazardousvehicleobjectdetectionalgorithm |