Helmet detection based on ODGSconv-YOLOv8s algorithm

Detecting safety helmets and reflective clothing holds paramount importance in railroad safety operations. However, the intricate and dynamic nature of railroad construction environments often poses challenges for existing detection methods, resulting in issues such as false positives, misses, and suboptimal detection efficiency. Hence, we introduce the ODGSconv-Yolov8s algorithm for enhancing Yolov8s in safety helmet and reflective clothing detection. Initially, we present a highly efficient network architecture, ODGSconv, which reinforces the Neck component to ensure both accuracy and generalization while significantly reducing parameter count. Next, SimSPPF replaces the SPPF in the Backbone to integrate multi-scale features and mitigate the detection leakage rate. The experimental findings demonstrate that the algorithm achieves a mean Average Precision (mAP) of 92.0%, an accuracy of 91.9%, and a recall of 87.9%, and can accurately detect helmets and reflective clothing.