Enhancing hazardous material vehicle detection with advanced feature enhancement modules using HMV-YOLO

Abstract

The transportation of hazardous chemicals on roadways has raised significant safety concerns. Incidents involving these substances often lead to severe and devastating consequences. Consequently, there is a pressing need for real-time detection systems tailored for hazardous material vehicles. However, existing detection methods face challenges in accurately identifying smaller targets and achieving high precision. This paper introduces a novel solution, HMV-YOLO, an enhancement of the YOLOv7-tiny model designed to address these challenges. Within this model, two innovative modules, CBSG and G-ELAN, are introduced. The CBSG module's mathematical model incorporates components such as Convolution (Conv2d), Batch Normalization (BN), SiLU activation, and Global Response Normalization (GRN) to mitigate feature collapse issues and enhance neuron activity. The G-ELAN module, building upon CBSG, further advances feature fusion. Experimental results showcase the superior performance of the enhanced model compared to the original one across various evaluation metrics. This advancement shows great promise for practical applications, particularly in the context of real-time monitoring systems for hazardous material vehicles.