Abstract: In industrial production, safety helmet provides a better safety guarantee for human head. In the field environment, the inspection of whether the construction personnel wear safety helmet mainly depends on manual inspection, so the efficiency is very low. In order to solve the problem of helmet detection and identification in construction site, this paper proposes a helmet detection method based on the deep cascade network model. Firstly, the construction personnel are detected through the You Only Look Once version 4 (YOLOv4) detection network. Then, the attention mechanism residual classification network is used to classify and judge the ROI region of personnel and identify whether they wear a helmet or not. This method is carried out in the experimental environment of Ubuntu18.04 system and Pytorch deep learning framework, and training and testing experiments are carried out in the self-produced helmet data set. The experimental results show that compared with YOLOv4, the safety helmet recognition model based on the deep cascade network has an accuracy increase of 2 percentage points, which effectively improves the safety helmet detection effect of construction personnel.

Key words: safety helmet, cascade network, target detection, You Only Look Once version 4(YOLOv4), residual classification network, attention mechanism