改进YOLOv8s与DeepSORT的矿工帽带检测及人员跟踪

doi:10.3778/j.issn.1002-8331.2310-0280

摘要/Abstract

摘要： 不系帽带，安全帽等于没戴。然而现有的安全帽检测方法，缺乏对帽带异常佩戴的检测研究。针对此问题，结合煤矿井下特殊的作业环境，以人员安全帽帽带检测及人员跟踪为研究对象，提出了CM-YOLOv8s算法检测安全帽及其帽带，利用DeepSORT算法对未系帽带的作业人员进行跟踪。利用井下监控视频制作数据集，使用CM-YOLOv8s对井下人员安全帽帽带进行检测：在YOLOv8s的基础上引入更高分辨率的特征图并新增了一种级联查询机制，在不提高计算成本的前提下能完成对小物体更精准的检测。利用改进DeepSORT对人员进行编码追踪：采用更深层卷积替换DeepSORT中小型残差网络来强化外观信息提取能力。通过自制井下安全帽帽带检测及跟踪数据集对改进算法进行验证，实验结果表明：CM-YOLOv8s的安全帽帽带识别算法平均精度均值达到92.3%，较YOLOv8s提高4.2个百分点。此外，基于CM-YOLOv8s与DeepSORT的安全帽规范佩戴识别系统的平均准确率为85.37%，检测速度达到59?FPS。提出的安全帽帽带检测算法，通过检测帽带是否在人员下颚附近来鉴别安全帽是否规范佩戴，能较好地平衡检测速度与精度，并能适应复杂的井下环境。通过在陈四楼煤矿数月的应用表明，实现了对安全帽佩戴异常的监测预警，加强了对矿工规范佩戴安全帽的有效监管。

关键词: 安全帽, 帽带检测, 实时监测, YOLOv8, DeepSORT

Abstract: Ensuring proper safety helmet usage is of utmost importance in underground mining inspections to protect workers. However, challenging conditions, such as high temperatures, often lead to non-compliant helmet wearing behavior. Existing detection methods are insufficient for underground environments, resulting in low recognition accuracy and inadequate detection of correctly worn helmets. To address these issues, this paper proposes an improved version of the CM-YOLOv8s algorithm that focuses on the chin strap as a small target for safety helmet detection and compliance assessment. The DeepSORT algorithm is then employed to track workers who fail to comply with helmet-wearing regulations. To begin, a comprehensive dataset is curated utilizing underground surveillance cameras. The CM-YOLOv8s algorithm is leveraged for safety helmet detection by incorporating higher-resolution feature maps and introducing a cascaded query mechanism. This approach enables precise detection of small targets without significantly increasing computational costs. Furthermore, the enhanced DeepSORT algorithm is employed for person tracking by replacing the small residual network in DeepSORT with deeper convolutional layers, thereby enhancing the extraction of appearance information. The proposed algorithm is validated using a self-made dataset for underground safety helmet detection and tracking. Experimental results demonstrate that CM-YOLOv8s achieves an average precision of 92.3% for safety helmet recognition, which is a 4.2 percentage points improvement over YOLOv8s. Additionally, the average accuracy of the safety helmet compliance recognition system, based on CM-YOLOv8s and DeepSORT, is 85.37%, with a detection speed of 59 FPS. The proposed algorithm effectively addresses compliance detection in safety helmet wearing by accurately assessing the position of the chin strap in proximity to the individual’s jaw. It strikes an optimal balance between detection speed and accuracy while exhibiting robust adaptability to the complex underground environments. The successful implementation of this algorithm at the Chensilou Coal Mine over an extended period has demonstrated its efficacy in monitoring and providing early warnings for abnormal safety helmet wearing, thereby bolstering regulatory oversight and promoting the compliant use of safety helmets among miners. The algorithm holds great potential for enhancing safety measures in underground mining inspections and can be applied to similar industrial scenarios. Further research and development in this direction are warranted to expand its applicability and impact.

Key words: safety helmet, chin strap detection, real-time monitoring, YOLOv8, DeepSORT

丁玲, 缪小然, 胡建峰, 赵作鹏, 张新建. 改进YOLOv8s与DeepSORT的矿工帽带检测及人员跟踪[J]. 计算机工程与应用, 2024, 60(5): 328-335.

DING Ling, MIAO Xiaoran, HU Jianfeng, ZHAO Zuopeng, ZHANG Xinjian. Improved Miner Chin Strap Detection and Personnel Tracking with YOLOv8s and DeepSORT[J]. Computer Engineering and Applications, 2024, 60(5): 328-335.

参考文献

[1] 张文杰, 黄体伟. 我国煤矿安全法规体系的现状及展望 [J]. 煤矿安全, 2020, 51(10): 10-17.
ZHANG W J, HUANG T W. Current situation and prospect of coal mine safety regulation system in China[J]. Coal Mine Safety, 2020, 51(10): 10-17.
[2] 李磊, 曹礼荣, 李世银, 等. 非正式组织视角下领导行为对煤矿工人不安全行为博弈分析[J]. 西安科技学报, 2022, 42(6): 1088-1095.
LI L, CAO L R, LI S Y, et al. Game analysis of coal miners’ unsafe behaviors based on informal organization leadership [J]. Journal of Xi’an University of Science and Technology, 2022, 42(6): 1088-1095.
[3] SINGH N, GUNJAN V K, CHAUDHARY G, et al. IoT enabled helmet to safeguard the health of mine workers[J]. Computer Communications, 2022, 193: 1-9.
[4] 张阳婷, 黄德启, 王东伟, 等. 基于深度学习的目标检测算法研究与应用综述[J]. 计算机工程与应用, 2023, 59(18): 1-13.
ZHANG Y T, HUANG D Q, WANG D W, et al. Review on research and application of deep learning-based target detection algorithms[J]. Computer Engineering and Applications, 2023, 59(18): 1-13.
[5] DONG S, WANG P, ABBAS K. A survey on deep learning and its applications[J]. Computer Science Review, 2021, 40: 318-322.
[6] 贾澎涛, 贾伟. 煤矿井下视频多目标轨迹跟踪算法研究[J]. 计算机工程与应用, 2018, 54(2): 222-227.
JIA P T, JIA W. Recherche algorithm on coal mine multi-target trajectory tracking[J]. Computer Engineering and Applications, 2018, 54(2): 222-227.
[7] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014: 580-587.
[8] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015: 1440-1448.
[9] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[10] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 779-788.
[11] TAN M X, LE Q V. EfficientNetV2: smaller models and faster training[C]//International Conference on Machine Learning (ICML), 2021: 215-223.
[12] ZHU X K, LYU S C, WANG X, et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021: 978-992.
[13] GU Y W, XU S K, WANG Y R, et al. An advanced deep learning approach for safety helmet wearing detection[C]//IEEE Int Congr on Cybermat (CYBER), 2019: 102-113.
[14] 王建波, 武友新. 改进YOLOv4-tiny的安全帽佩戴检测算法[J]. 计算机工程与应用, 2023, 59(4): 183-190.
WANG J B, WU Y X. Safety helmet wearing detection algorithm of improved YOLOv4-tiny[J]. Computer Engineering and Applications, 2023, 59(4): 183-190.
[15] 宋晓凤, 吴云军, 刘冰冰, 等. 改进YOLOv5s算法的安全帽佩戴检测[J]. 计算机工程与应用, 2023, 59(2): 194-201.
SONG X F, WU Y J, LIU B B, et al. Improved YOLOv5s algorithm for helmet wearing detection[J]. Computer Engineering and Applications, 2023, 59(2): 194-201.
[16] 王玲敏, 段军, 辛立伟. 引入注意力机制的YOLOv5安全帽佩戴检测方法[J]. 计算机工程与应用, 2022, 58(9): 303-312.
WANG L M, DUAN J, XIN L W. YOLOv5 helmet wear detection method with introduction of attention mechanism[J]. Computer Engineering and Applications, 2022, 58(9): 303-312.
[17] ZHAO Z P, HE C, ZHAO G M, et al. RA-YOLOX: re-parameterization align decoupled head and novel label assignment scheme based on YOLOX[J]. Pattern Recognition, 2023, 140: 22-26.
[18] WU F, JIN G Q, GAO M Y, et al. Helmet detection based on improved YOLOv3 deep model[C]//Proceedings of the IEEE International Conference on Networking, 2019: 650-658.
[19] SONG H J, ZHANG X H, SONG J, et al. Detection and tracking of safety helmet based on DeepSort and YOLOv5[J]. Multimedia Tools and Applications, 2023, 82(7): 10781-10794.