Object Detection for Hazardous Material Vehicles Based on Improved YOLOv5 Algorithm

Author:

Zhu Pengcheng1,Chen Bolun12,Liu Bushi1,Qi Zifan1,Wang Shanshan1,Wang Ling1

Affiliation:

1. Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China

2. Department of Physics, University of Fribourg, CH-1700 Fribourg, Switzerland

Abstract

Hazardous material vehicles are a non-negligible mobile source of danger in transport and pose a significant safety risk. At present, the current detection technology is well developed, but it also faces a series of challenges such as a significant amount of computational effort and unsatisfactory accuracy. To address these issues, this paper proposes a method based on YOLOv5 to improve the detection accuracy of hazardous material vehicles. The method introduces an attention module in the YOLOv5 backbone network as well as the neck network to achieve the purpose of extracting better features by assigning different weights to different parts of the feature map to suppress non-critical information. In order to enhance the fusion capability of the model under different sized feature maps, the SPPF (Spatial Pyramid Pooling-Fast) layer in the network is replaced by the SPPCSPC (Spatial Pyramid Pooling Cross Stage Partial Conv) layer. In addition, the bounding box loss function was replaced with the SIoU loss function in order to effectively speed up the bounding box regression and enhance the localization accuracy of the model. Experiments on the dataset show that the improved model has effectively improved the detection accuracy of hazardous chemical vehicles compared with the original model. Our model is of great significance for achieving traffic accident monitoring and effective emergency rescue.

Funder

Humanities and Social Sciences Project of the Ministry of Education of China

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Natural Science Foundation of Education Department of Jiangsu Province

Six talent peaks project in Jiangsu Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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