G-YOLO: A YOLOv7-based target detection algorithm for lightweight hazardous chemical vehicles

Abstract

Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.The method first introduces a lightweight feature extraction structure, E-GhostV2 network, into the trunk and neck of the model to achieve effective feature extraction while reducing the burden of the model. Additionally, the PConv is used in the model’s backbone to effectively reduce redundant computations and memory access, thereby enhancing efficiency and feature extraction capabilities. Furthermore, to address the problem of performance degradation caused by overemphasizing high-quality samples, the model adopts the WIoU loss function, which balances the training effect of high-quality and low-quality samples, enhancing the model’s robustness and generalization performance. Experimental results demonstrate that the improved model achieves satisfactory detection accuracy while reducing the number of model parameters, providing robust support for theoretical research and practical applications in the field of hazardous chemical vehicle object detection.