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

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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.

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Data availability

The datasets generated and analyzed during the current study are available in the https://www.kaggle.com/datasets/andrewmvd/hard-hat-detection

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Funding

This work was supported in part by the Chunhui Project of Ministry of Education in China under Grant z2018087, Chunhui Project of Ministry of Education in China under Grant [2019]1383, and in part by the Science and Technology Achievements Transfer and Transformation Demonstration project of Sichuan province in China under Grant 2020ZHCG0099.

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Authors and Affiliations

  1. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

    Gang Dong, Yefei Zhang, Weicheng Xie & Yong Huang

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  1. Gang Dong
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  2. Yefei Zhang
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  4. Yong Huang
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Contributions

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by G.D., W.X., and Y.Z. The first draft of the manuscript was written by G.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Part of the new experiments and literature search were completed by Y.H.

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Correspondence to Weicheng Xie.

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Dong, G., Zhang, Y., Xie, W. et al. A safety helmet-wearing detection method based on cross-layer connection. J Real-Time Image Proc 21, 72 (2024). https://doi.org/10.1007/s11554-024-01437-5

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