Workshop safety helmet detection algorithm based on improved YOLOv5

Abstract

For workshop workers, helmets are one of the most important protective tools. Wearing helmets during operation can further protect the lives of workers. Aiming at the situation that the current safety helmet detection model has missed detection and false detection of small targets and dense targets in complex environments such as workshops, this paper proposes a workshop operation safety helmet detection algorithm based on improved YOLOv5 in combination with practical application scenarios. The CBAM attention mechanism is added to the backbone network, so that the network pays more attention to the safety helmet target to be detected, which can improve the detection effect in complex backgrounds. The EIoU _ Loss function is used to replace the GIoU _ Loss function to improve the convergence effect of the module. By comparing the accuracy and detection speed of the improved YOLOv5 target detection algorithm with the original YOLOv5 target detection algorithm on the self-made safety helmet detection data set, the results show that the average accuracy of the improved YOLOv5 model is 1.4 % higher than that of the original model. Achieved in a complex environment for small targets and dense target detection requirements.