A safety helmet-wearing detection method based on cross-layer connection

Abstract

Given the current safety helmet detection methods, the feature information of the small-scale safety helmet will be lost after the network model is convolved many times, resulting in the problem of missing detection of the safety helmet. To this end, an improved target detection algorithm of YOLOv5 is used to detect the wearing of safety helmets. Firstly, a new small-scale detection layer is added to the head of the network for multi-scale feature fusion, thereby increasing the receptive field area of the feature map to improve the model’s recognition of small targets. Secondly, a cross-layer connection is designed between the feature extraction network and the feature fusion network to enhance the fine-grained features of the target in the shallow layer of the network. Thirdly, a coordinate attention (CA) module is added to the cross-layer connection to capture the global information of the image and improve the localization ability of the target. Finally, the Normalized Wasserstein Distance (NWD) is used to measure the similarity between bounding boxes, replacing the intersection over union (IoU) method. The experimental results show that the improved model achieves 95.09% of the mAP value for safety helmet-wearing detection, which has a good effect on the recognition of small-sized safety helmets of different degrees in the construction work scene.