Safety Helmet‐Wearing Detection System for Manufacturing Workshop Based on Improved YOLOv7
Xiaowen Chen, Qingsheng Xie
Abstract
Safety helmets play a vital role in protecting workers’ heads. In order to improve the accuracy of the detection model in complex environments, such as complex backgrounds and different lighting and distances, we propose a safety helmet‐wearing detection algorithm based on the improved YOLOv7. In the backbone network, 16‐channel features are used to replace 3‐channel RGB features. Structured pruning is performed in the head network, and the loss function is replaced by SIoU. Experiments on the “helmet‐head,” “helmet‐data,” and “helmet” data sets show that the mAP and F1 of YOLOv7_ours improved in this paper are better than Faster RCNN, YOLOv5, and YOLOv7 series models. On image data of different application scenarios, light intensity, and color depth, YOLOv7_ours has better stability and higher accuracy and can detect at 112.4FPS (1000/8.9). Based on the improved YOLOv7_ours, we integrated face recognition technology and text‐to‐speech (TTS) to realize helmet detection, identity recognition, and automatic voice reminder capabilities and developed a safety helmet‐wearing detection prototype system. We verified the feasibility of the helmet detection algorithm and system in the semifinished product manufacturing workshop.