Helmet-wearing recognition algorithm for coal mine underground operation scenarios

doi:10.16265/j.cnki.issn1003-3033.2024.03.1985

Abstract

Abstract:

To improve the accuracy of miners' helmet-wearing recognition in coal mine underground operations, a helmet-wearing recognition algorithm was proposed based on human posture analysis and machine vision system optimization methods. First, Single Shot MultiBox Detector (SSD) was used as the basic model of multi-target identification. The squeezed neural network (SqueezeNet) was used to reduce the model parameters to develop an efficient recognition model, which improved the recognition accuracy of the miners' helmet and maintained the balance between the recognition accuracy and the calculation speed. Then, a multi-person posture estimation algorithm was used to locate the joint points of each miner and determine the miners' complex behavioral status. Finally, the upper limb nodes of the target were extracted based on fusion model of multi-target recognition and multi-person posture estimation, and then the helmet-wearing condition was determined by the spatial topological relationship between the upper limb nodes and the helmet frame. Moreover, 3 000 pieces of image data were selected to validate the proposed method's performance. The results indicated that the machine vision system can optimize the hardware and software configuration to improve the system's performance. Furthermore, the identification accuracy reached up to 91.1%, which was much better than that of the latest helmet-wearing recognition algorithm. Therefore, the proposed system in this study can meet the requirements of helmet-wearing recognition accuracy for underground coal miners.

Key words: coal mine underground, helmet-wearing, recognition algorithm, posture analysis, model optimization

CLC Number:

X936

ZUO Mingcheng, JIAO Wenhua. Helmet-wearing recognition algorithm for coal mine underground operation scenarios[J]. China Safety Science Journal, 2024, 34(3): 237-246.

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层	尺寸与个数	层	尺寸与个数
卷积层2-1	3×3×51	火模型9-Loc	3×3×24
卷积层2-2	3×3×46	火模型9-Con	3×3×12
卷积层3-1	3×3×55	火模型10-Loc	3×3×24
卷积层3-2	3×3×85	火模型10-Con	3×3×12
火模型4-Loc	3×3×16	卷积层2-2-Loc	3×3×24
火模型4-Con	3×3×8	卷积层2-2-Con	3×3×12
火模型8-Loc	3×3×24	卷积层3-2-Loc	3×3×16
火模型8- Con	3×3×12	卷积层3-2-Con	3×3×8

算法	识别准确率/%	行为检测效果
所提算法	91.1	可检测全部行为
YOLOv8	86.8	仅站立、坐立
YOLOv7	85.1	仅站立、坐立
增强的Faster R-CNN	86.6	可检测全部行为
融合姿态分析的YOLOv4	87.9	可检测全部行为
改进YOLOv5	87.2	仅站立、坐立
多策略YOLOv5	85.7	仅站立、坐立
Faster R-CNN	84.3	仅站立、坐立
Vibe-DPM	80	仅站立、坐立

模型	精度/%	尺寸/MB	速度/(帧·s^-1)
原始SSD模型	45	82.1	33
微调SSD模型	72.7	82.1	33
SqueezeNet-SSD	91.1	21.1	100