Abstract
Wearing helmets correctly is crucial for the safety of workers in industrial and construction settings. This paper introduces an improved YOLOv7 algorithm that is designed to achieve efficient real-time detection of the usage and deployment of safety helmets. The improved YOLOv7 algorithm utilizes ReXNet as its backbone network and enhances the feature extraction ability of the constructed model by modifying the feature output layer of the network. To address the challenge of small target detection, the P2 detection layer is added to the backbone network to mitigate the loss of small target features. Furthermore, an asymptotic feature pyramid network (AFPN) is introduced in the neck part to facilitate direct interaction among the nonadjacent layers and feature fusion. Additionally, the channel pruning algorithm is applied to simplify the improved YOLOv7 detection model, which significantly reduces the number of model parameters, the model size, and the number of floating-point operations (FLOPs) by 74.5%, 73.4%, and 52.0%, respectively. The size of the pruned model is only 19.9 MB. Compared with 8 mainstream algorithms, this algorithm has better performance in terms of both accuracy and efficiency. Finally, by deploying the trained model on edge development equipment, validating the effectiveness of this helmet detection algorithm at industrial sites. In summary, the proposed lightweight helmet detection algorithm based on an improved YOLOv7 network satisfies the real-time requirements imposed in the field, providing technical support for safety inspection tasks in complex industrial environments.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03584-5/MediaObjects/11760_2024_3584_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03584-5/MediaObjects/11760_2024_3584_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03584-5/MediaObjects/11760_2024_3584_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03584-5/MediaObjects/11760_2024_3584_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03584-5/MediaObjects/11760_2024_3584_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03584-5/MediaObjects/11760_2024_3584_Fig6_HTML.jpg)
Similar content being viewed by others
Data Availability
The SHWD is available at https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset and was accessed on 17 December 2019. The FHPD will not be made available on the website.
References
An, Q., Xu, Y., Yu, J., Tang, M., Liu, T., Xu, F.: Research on safety helmet detection algorithm based on improved yolov5s. Sensors 23(13), 5824 (2023). https://doi.org/10.3390/s23135824
Li, T., Xu, H., Bai, J.: A lightweight safety helmet detection network based on bidirectional connection module and polarized self-attention. In: International Conference on Neural Information Processing, pp. 253–264 (2023). https://doi.org/10.1007/978-981-99-8073-4_20
Zhang, H., Yan, X., Li, H., Jin, R., Fu, H.F.: Real-time alarming, monitoring, and locating for non-hard-hat use in construction. J. Constr. Eng. Manage. 145(3), 04019006 (2019). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001629
Li, J., Liu, H., Wang, T., Jiang, M., Wang, S., Li, K., Zhao, X.: Safety helmet wearing detection based on image processing and machine learning. In: 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), pp. 201–205 (2017). https://doi.org/10.1109/ICACI.2017.7974509
Kai, Z., Xiaozhi, W.: Wearing safety helmet detection in substation. In: 2019 IEEE 2nd International Conference on Electronics and Communication Engineering (ICECE), pp. 206–210 (2019). https://doi.org/10.1109/ICECE48499.2019.9058524
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014). https://doi.org/10.48550/arXiv.1311.2524
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intel. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Xu, J.: Safety helmet monitoring of power grid staff based on improved yolov3. Mechatronics and Automation Technology: Proceedings of ICMAT 2022 33, 58 (2023) https://doi.org/10.3233/ATDE221150
Chen, J., Deng, S., Wang, P., Huang, X., Liu, Y.: Lightweight helmet detection algorithm using an improved yolov4. Sensors 23(3), 1256 (2023). https://doi.org/10.3390/s23031256
Song, H., Zhang, X., Song, J., Zhao, J.: Detection and tracking of safety helmet based on deepsort and yolov5. Multimed. Tool. Appl. 82(7), 10781–10794 (2023). https://doi.org/10.1007/s11042-022-13305-0
Han, J., Liu, Y., Li, Z., Liu, Y., Zhan, B.: Safety helmet detection based on yolov5 driven by super-resolution reconstruction. Sensors 23(4), 1822 (2023). https://doi.org/10.3390/s23041822
Chen, X., Xie, Q., et al.: Safety helmet-wearing detection system for manufacturing workshop based on improved yolov7. J. Sensors (2023). https://doi.org/10.1155/2023/7230463
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023). https://doi.org/10.48550/arXiv.2207.02696
Han, D., Yun, S., Heo, B., Yoo, Y.: Rethinking channel dimensions for efficient model design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 732–741 (2021). https://doi.org/10.48550/arXiv.2007.00992
Yang, G., Lei, J., Zhu, Z., Cheng, S., Feng, Z., Liang, R.: Afpn: Asymptotic feature pyramid network for object detection. arXiv preprint arXiv:2306.15988 (2023) https://doi.org/10.1155/2023/7230463
Zhang, J., Zhang, R., Shu, X., Yu, L., Xu, X.: Channel pruning-based yolov7 deep learning algorithm for identifying trolley codes. Appl. Sci. 13(18), 10202 (2023). https://doi.org/10.3390/app131810202
njvisionpower: Safety-Helmet-Wearing-Dataset. (2019). https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580–1589 (2020). https://doi.org/10.1109/CVPR42600.2020.00165
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: Efficientvit: Memory efficient vision transformer with cascaded group attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14420–14430 (2023). https://doi.org/10.48550/arXiv.2305.07027
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019). https://doi.org/10.48550/arXiv.1905.02244
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) https://doi.org/10.48550/arXiv.1804.02767
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020) https://doi.org/10.48550/arXiv.2004.10934
Ultralytics: YOLOv5. (2021). https://github.com/ultralytics/yolov5
WongKinYiu: YOLOv7. (2023). https://github.com/WongKinYiu/yolov7
Ultralytics: YOLOv8. (2023). https://gitcode.com/mirrors/ultralytics/ultralytics
Acknowledgements
Research conducted for this article was supported by the Major Science and Technology Projects of Anhui Province, China (grant number 2020b05050002).
Author information
Authors and Affiliations
Contributions
Ruihao Liu: Methodology, Software, Supervision, Writing-original draft. Zhongxi Shao: Methodology, Supervision, Formal analysis. Zhenzhong Yu: Supervision, Funding acquisition, Investigation. Rui Li: Data collection, Validation. All authors contributed to writing and revising the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there are no conflict of interest.
Ethical approval
The study followed ethical guidelines and obtained informed consent from all participants involved in data collection. All procedures were conducted in accordance with Springer’s ethical standards.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, R., Shao, Z., Yu, Z. et al. Research on real-time helmet detection and deployment based on an improved YOLOv7 network with channel pruning. SIViP 19, 118 (2025). https://doi.org/10.1007/s11760-024-03584-5
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11760-024-03584-5