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A one-stage deep learning framework for automatic detection of safety harnesses in high-altitude operations

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Abstract

Safety harness plays an essential role in protecting the workers in high-altitude operations from falls from heights. Automatic detection of safety harness wearing is significant for safety management. To deal with the inherent problems of the existing two-stage detection method for safety harnesses, a novel one-stage detection framework is designed by incorporating several promising modules into a YOLO network, which is end-to-end trained. Here, the dilated convolution module and the depth-wise separable convolution module are subsequently incorporated to improve the overall receptive fields of feature maps and to reduce the amount of calculation, respectively. An attention proposal sub-network (APN) is introduced for fine-grained feature learning. To improve the convergence of the proposed framework, a novel loss function is designed by adding a penalty term into the loss function named complete intersection over union (CIoU). Also, to facilitate the study, a new and publicly available dataset for safety harness wearing detection is constructed, which consists of 2617 images including 8163 safety harness examples. Experimental results demonstrate that the proposed framework can perform an excellent task for safety harness wearing detection with 80.25% mAP at a reasonable speed of 29.18 FPS, especially for small instances.

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Acknowledgements

This work was in part supported by the National Natural Science Foundation of China (Nos. 62171142 and 61901123), and the Research Fund for Colleges and Universities in Huizhou (No. 2019HZKY003).

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Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Liefan Wu, Nian Cai, Zhijian Liu & An Yuan

  2. State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, 510006, People’s Republic of China

    Nian Cai & Han Wang

  3. Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co., Ltd., Huizhou, 516025, China

    Nian Cai

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  1. Liefan Wu
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  2. Nian Cai
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  3. Zhijian Liu
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  4. An Yuan
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  5. Han Wang
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Correspondence to Nian Cai.

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Wu, L., Cai, N., Liu, Z. et al. A one-stage deep learning framework for automatic detection of safety harnesses in high-altitude operations. SIViP 17, 75–82 (2023). https://doi.org/10.1007/s11760-022-02205-3

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