Research on helmet detection algorithm based on improved YOLOv4-tiny

Abstract

In order to effectively supervise the wearing of safety helmets by construction personnel, the YOLOv4-tiny target detection algorithm is used to detect the wearing of safety helmets. A lightweight model with higher accuracy and less computation is designed for YOLOv4-tiny, which is more suitable for real-time helmet wearing detection. Firstly, G-Resblock is designed to replace Resblock to reduce the computational complexity of the model and occupy less computing resources. However, YOLOv4-tiny is prone to error detection or missed detection in complex work scenarios. In order to solve this problem, an attention mechanism is added to YOLOv4-tiny, the serial channel of CBAM is improved as a parallel channel, and P-CBAM is added to YOLOv4-tiny to solve the problem of poor model detection effect. The improved YOLOv4-tiny can better complete the helmet detection task.