Tunnel worker detection method based on improved YOLOv5s

This research provides an enhanced YOLOv5s tunnel workers identification algorithm to address the challenge of complicated and challenging worker distribution in tunnel environments. The network's ability to extract features is improved by incorporating a tiny target detection layer and the Squeeze-and-Excitation (SE) attention mechanism network. The introduction of depthwise separable convolution helps to prevent having a lot of model parameters. In order to decrease the missing likelihood of workers detection, the Soft-NMS algorithm is implemented with the aim of overlapping workers' target features in certain photos taken during tunnel construction. The experimental results demonstrate that the tunnel worker detection method proposed in this paper can successfully detect workers in the tunnel construction environment, with the precision rate of the improved detection model increasing from 90.06% to 94.03%, the recall rate increasing from 88.28% to 92.18%, and the average precision increasing from 83.95% to 86.91%. It is of vital significance to the life safety of tunnel workers.