Citation: | ZHANG Lei,SUN Zhipeng,TAO Hongjing,et al. Research on real-time monitoring method of mine personnel protective equipment with improved YOLOv8[J]. Coal Science and Technology,2025,53(S1):354−365. DOI: 10.12438/cst.2024-0142 |
Wearing personal protective equipment is an important means to ensure the safety of mine personnel. It is an important task of mine safety management to carry out mine personnel protective equipment monitoring. Coal mine underground environment is more complex, video surveillance is susceptible to noise, light and dust and other factors interference, resulting in the existing target detection methods for mine personnel protective equipment there are low detection accuracy, poor real-time, model complexity and so on, proposed an improvement of YOLOv8 real-time monitoring of mine personnel protective equipment method, known as DBE-YOLO. The DBE-YOLO model is first combined with deformable Convolution (DCNv2) in the CBS module of the benchmark model backbone network to form a DBS module. Making convolution deformable, when sampling, it can more closely detect the true shape and size of the object, more robust, It effectively improves its feature acquisition ability for targets of different scales. It is beneficial for the model to extract more feature information of personnel protective equipment and improve the model detection. Secondly, the weighted bidirectional feature pyramid mechanism (BiFPN) is integrated in the feature enhancement network. In the process of multi-scale feature fusion, the less efficient feature transmission nodes are deleted. Achieve a higher level of integration, the fusion efficiency of different scale features is improved. BiFPN also introduces a weight that can be learned. Helps the network learn the importance of different input features. Finally, WIoUv3 is used as the loss function of the model. By dynamically distributing gradient gain, Focus on ordinary anchor frame quality, In the process of model training, the harmful gradient generated by low quality anchor frame is reduced. The model performance is further improved. The experimental results show that DBE-YOLO model has a good effect in the monitoring of mine personnel protective equipment. The accuracy, recall and average accuracy were 93.1%, 93.0% and 95.8%, respectively. Compared with the benchmark model, it was increased by 0.8%, 2.9% and 2.9% respectively. Detection real-time improved to 65 f·s−1, An increase of 8.3%, In addition, the number of parameters, floating point computation and model volume are 2 M, 6.6 G and 4.4 MB respectively. Compared with the original model, they were reduced by 33.3%, 18.5% and 30.2% respectively. The improved model is verified by using video surveillance of coal mine field operation. It effectively improves the problem of missing and false detection, It provides technical means for improving the operation safety of mine personnel.
[1] |
NATH N D,BEHZADAN A H,PAAL S G. Deep learning for site safety:Real-time detection of personal protective equipment[J]. Automation in Construction,2020,112:103085. doi: 10.1016/j.autcon.2020.103085
|
[2] |
李华,王岩彬,益朋,等. 基于深度学习的复杂作业场景下安全帽识别研究[J]. 中国安全生产科学技术,2021,17(1):175−181.
LI Hua,WANG Yanbin,YI Peng,et al. Research on recognition of safety helmets under complex operation scenes based on deep learning[J]. Journal of Safety Science and Technology,2021,17(1):175−181.
|
[3] |
陈一洲,杨锐,苏国锋,等. 应急装备资源分类及管理技术研究[J]. 中国安全科学学报,2014,24(7):166−171.
CHEN Yizhou,YANG Rui,SU Guofeng,et al. Study on technologies for classifying and managing emergency equipment resources[J]. China Safety Science Journal,2014,24(7):166−171.
|
[4] |
刘晓慧,叶西宁. 肤色检测和Hu矩在安全帽识别中的应用[J]. 华东理工大学学报(自然科学版),2014,40(3):365−370.
LIU Xiaohui,YE Xining. Skin color detection and hu moments in helmet recognition research[J]. Journal of East China University of Science and Technology (Natural Science Edition),2014,40(3):365−370.
|
[5] |
张磊,李熙尉,燕倩如,等. 基于改进YOLOv5s的综采工作面人员检测算法[J]. 中国安全科学学报,2023,33(7):82−89.
ZHANG Lei,LI Xiwei,YAN Qianru,et al. Personnel detection algorithm in fully mechanized coal face based on improved YOLOv5s[J]. China Safety Science Journal,2023,33(7):82−89.
|
[6] |
代少升,曾奇,黄炼,等. 基于S3-YOLOv5s的矿井人员防护设备检测算法研究[J]. 半导体光电,2023,44(1):153−160.
DAI Shaosheng,ZENG Qi,HUANG Lian,et al. Research on detection algorithm of mine personnel protection equipment based on S3-YOLOv5s[J]. Semiconductor Optoelectronics,2023,44(1):153−160.
|
[7] |
程换新,蒋泽芹,程力,等. 基于改进YOLOX-S的安全帽反光衣检测算法[J]. 电子测量技术,2022,45(6):130−135.
CHENG Huanxin,JIANG Zeqin,CHENG Li,et al. Helmet and reflective clothing detection algorithm based on improved YOLOX-S[J]. Electronic Measurement Technology,2022,45(6):130−135.
|
[8] |
王媛彬,韦思雄,吴华英,等. 基于改进YOLOv5s的矿井下安全帽佩戴检测算法[J/OL]. (2024−03−23)[2024−04−23]. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=MTKJ20240320006&dbname=CJFD&dbcode=CJFQ.
WANG Yuanbin,WEI Sixiong,WU Huaying,et al. Detection algorithm of helmet wearing in underground mine based on improved YOLOv5s[J/OL]. (2024−03−23)[2024−04−23]. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=MTKJ20240320006&dbname=CJFD&dbcode=CJFQ.
|
[9] |
GIRSHICK R,DONAHUE J,DARRELL T,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway,NJ:IEEE,2014:580−587.
|
[10] |
GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). Piscataway,NJ:IEEE,2015:1440−1448.
|
[11] |
REN S Q,HE K M,GIRSHICK R,et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137−1149. doi: 10.1109/TPAMI.2016.2577031
|
[12] |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 2016 European Conference on Computer Vision. Amsterdam: Springer International Publishing AG Cham, 2016: 21‒37.
|
[13] |
REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:Unified,real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway,NJ:IEEE,2016:779−788.
|
[14] |
WANG C Y,MARK LIAO H Y,WU Y H,et al. CSPNet:A new backbone that can enhance learning capability of CNN[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE,2020:1571−1580.
|
[15] |
TAN M X,PANG R M,LE Q V. EfficientDet:Scalable and efficient object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,2020:10778−10787.
|
[16] |
DAI J F,QI H Z,XIONG Y W,et al. Deformable convolutional networks[C]//2017 IEEE International Conference on Computer Vision (ICCV). Piscataway,NJ:IEEE,2017:764−773.
|
[17] |
LIN T Y,DOLLÁR P,GIRSHICK R,et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway,NJ:IEEE,2017:936−944.
|
[18] |
LIU S,QI L,QIN H F,et al. Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway,NJ:IEEE,2018:8759−8768.
|
[19] |
ZHENG Z H,WANG P,LIU W,et al. Distance-IoU loss:Faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(7):12993−13000. doi: 10.1609/aaai.v34i07.6999
|
[20] |
TONG Z J,CHEN Y H,XU Z W,et al. Wise-IoU:Bounding box regression loss with dynamic focusing mechanism[EB/OL]. (2023−11−07)[2024−01−24]. https://arxiv.org/abs/2301.10051v3.
|
[21] |
PIZER S M,AMBURN E P,AUSTIN J D,et al. Adaptive histogram equalization and its variations[J]. Computer Vision,Graphics,and Image Processing,1987,39(3):355−368. doi: 10.1016/S0734-189X(87)80186-X
|
[22] |
REDMON J,FARHADI A. YOLOv3:An incremental improvement[EB/OL]. (2018−04−08)[2024−01−24]. https://arxiv.org/abs/1804.02767v1.
|
[23] |
WANG C Y,BOCHKOVSKIY A,LIAO H M. YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway,NJ:IEEE,2023:7464−7475.
|
[24] |
吴利刚,陈乐,吕媛媛,等. 基于轻量化的输送带块煤实时监测方法[J]. 煤炭科学技术,2023,51(S2):285−293.
WU Ligang,CHEN Le,LYU Yuanyuan,et al. A lightweight-based method for real-time monitoring of lump coal on conveyor belts[J]. Coal Science and Technology,2023,51(S2):285−293.
|