Abstract
Vehicle detection and recognition is one of hotspots for intelligent transportation. The existing object detection and recognition methods have the problem of high accuracy for a single kind of object and low accuracy for multiple kinds of object. To solve this problem, an improved YOLOv5s-ATE network model is proposed in this paper. The coordinate attention (CA) is incorporate in YOLOv5s-ATE. Furthermore, SPPF is added to the neck of the model, and can effectively improve the accuracy of multiple object detection and recognition. When the trained network model is evaluated on VOC validation set, the mean accuracy of YOLOv5s-ATE mAP@0.5 and mAP@0.5:0.95 are improved. The proposed YOLOV5-ATE model can effectively carry out target detection and recognition, which improves the theoretical research and technical support for vehicle detection and recognition.
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Lu, X., Song, W. (2022). Improved YOLOv5s Model for Vehicle Detection and Recognition. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_35
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DOI: https://doi.org/10.1007/978-3-031-13832-4_35
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