SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm - Wang, Shanshan; Liu, Bushi; Zhu, Pengcheng; Meng, Xianchun; Chen, Bolun; Shao, Wei; Chen, Liqing (IET computer vision, 2024)
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. The proposed algorithm addresses challenges such as complex environments, low resolution, and small‐sized targets, making significant contributions to the detection of hazardous chemical vehicles. This is achieved through the introduction of the receptive field expansion block (RFEB) and the separable attention mechanism, enhancing the model's ability to capture detailed information and improve feature representation. Experimental results demonstrate that the improved model surpasses the baseline in terms of accuracy, achieving more precise object detection.