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主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于改进度量学习的煤矿井下行人重识别方法研究
  • Title

    Research on personnel re-recognition method in coal mine underground based on improved metric learning

  • 作者

    张立亚王寓郝博南

  • Author

    ZHANG Liya;WANG Yu;HAO Bonan

  • 单位

    煤炭科学技术研究院有限公司煤矿应急避险技术装备工程研究中心北京市煤矿安全工程技术研究中心中国传媒大学 信息与通信工程学院

  • Organization
    CCTEG China Coal Research Institute
    Engineering Research Center for Technology Equipment of Emergency Refuge in Coal Mine
    Beijing Engineering and Research Center of Mine Safe
    School of Information and Communication Engineering, Communication University of China
  • 摘要
    传统基于度量学习的煤矿井下行人重识别方法中,由于度量学习忽略正负样本绝对距离,造成损失函数梯度消失或梯度弥散,导致井下人员位置信息识别精度不高。针对该问题,提出了一种基于改进度量学习的煤矿井下行人重识别方法。首先,采用基于手工设计特征的井下人员特征提取方法,对颜色空间、纹理空间等特征进行手动加工提炼,丰富特征维度。然后,采用欧氏距离对人员高维特征进行相似性计算。最后,提出一种改进的三重损失函数,通过在传统三重损失函数中加入自适应权重,增加有效样本的权重,解决了由于忽略正负样本绝对距离导致的梯度消失或梯度弥散问题。将传统识别方法与基于改进度量学习的煤矿井下行人重识别方法进行了累积匹配特征曲线验证、识别速率验证,结果表明:① 基于改进度量学习的煤矿井下行人重识别方法在相似样本个数为50左右时,样本匹配概率达100%。② 在2种不同标定大小图像的推理耗时上,基于改进度量学习的煤矿井下行人重识别方法较传统重识别方法分别减少了44,68 ms。③ 基于改进度量学习的煤矿井下行人重识别方法在舍弃行人头脚部分图像后表现更好,在相似样本个数为42左右时,样本匹配概率达100%。
  • Abstract
    In the traditional personnel re-recognition method in coal mine underground based on metric learning, because metric learning ignores the absolute distance between positive and negative samples, the gradient of the loss function disappears or disperses. This results in low recognition precision of underground personnel position information. In order to solve this problem, a personnel re-recognition method in coal mine underground based on improved metric learning is proposed. Firstly, a feature extraction method for underground personnel based on manual design features is adopted to manually process and extract features such as color space and texture space, enriching the feature dimensions. Secondly, Euclidean distance is used to calculate the similarity of high-dimensional features of personnel. Finally, an improved triple loss function is proposed. Adding adaptive weights to the traditional triple loss function increases the weight of effective samples. It solves the problem of gradient disappearance or dispersion caused by ignoring the absolute distance between positive and negative samples. The traditional recognition method is compared with the personnel re-recognition method in coal mine underground based on improved metric learning for cumulative matching feature curve verification and recognition rate verification. The results show the following points. ① The personnel re-recognition method in coal mine underground based on improved metric learning has a sample matching probability of 100% when the number of similar samples is around 50. ② The personnel re-recognition method in coal mine underground based on improved metric learning reduces the inference time of two different calibration size images by 44 ms and 68 ms, respectively, compared to traditional re-recognition methods. ③ The personnel re-recognition method in coal mine underground based on improved metric learning performs better after discarding the images of personnel heads and feet. It has a sample matching probability of 100% when the number of similar samples is around 42.
  • 关键词

    矿井人员精确定位行人重识别度量学习相似性测量自适应三重损失函数累积匹配特征

  • KeyWords

    precise positioning of mine personnel;personnel re-recognition;metric learning;similarity measurement;adaptive triple loss function;cumulative matching feature

  • 基金项目(Foundation)
    天地科技股份有限公司科技创新创业资金专项项目(2022-2-TD-ZD001,2022-TD-ZD001,2023-TD-ZD005-005)。
  • DOI
  • 引用格式
    张立亚,王寓,郝博南. 基于改进度量学习的煤矿井下行人重识别方法研究[J]. 工矿自动化,2023,49(9):84-89, 166.
  • Citation
    ZHANG Liya, WANG Yu, HAO Bonan. Research on personnel re-recognition method in coal mine underground based on improved metric learning[J]. Journal of Mine Automation,2023,49(9):84-89, 166.
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    • 煤矿井下人员重识别流程

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