局部特征引导标签平滑与优化的井下弱特征人员重识别  

Local feature-guided label smoothing and optimization for re-identification of underground personnel with weak features

作　　者：张杰[1] 缪小然 赵作鹏[2] 胡建峰 闵冰冰 高宇蒙 ZHANG Jie;MIAO Xiaoran;ZHAO Zuopeng;HU Jianfeng;MIN Bingbing;GAO Yumeng(Xingdong Mine,Jizhong Energy Co.,Ltd.,Xingtai 054000,China;School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221006,China)

机构地区：[1]冀中能源股份有限公司邢东矿,河北邢台054000 [2]中国矿业大学计算机科学与技术学院,江苏徐州221006

出　　处：《工矿自动化》2024年第2期83-89,共7页Journal Of Mine Automation

基　　金：国家自然科学基金资助项目(61976217)。

摘　　要：煤矿井下低照度、强光扰、高粉尘等环境条件,以及井下人员服装的相似性和脸部落煤现象,导致井下弱特征人员重识别困难。现有人员重识别方法仅提取全局特征,未充分考虑局部特征,使得井下人员重识别准确率较低。针对上述问题,提出了一种局部特征引导标签平滑与优化的井下弱特征人员重识别方法。该方法首先通过卷积神经网络提取井下人员图像的全局特征与局部特征;然后利用k最近邻相似性计算全局特征和局部特征的互补性得分,来衡量全局特征和局部特征的相似程度;最后根据特征互补性得分对局部特征进行标签平滑及对全局特征进行标签优化,即动态调整每个局部特征的权重,以改进每个局部特征的标签,并对局部特征的预测结果进行汇总,利用更可靠的信息来完善标签以作为全局特征的标签,从而减少图像噪声并增强特征识别能力。实验结果表明,该方法在公开数据集和包含井下人员图像的自建数据集上的平均精度均值(mAP)、第一匹配正确率(Rank-1)和平均逆置负样本惩罚率(mINP)总体优于主流人员重识别方法,具有良好的泛化性和鲁棒性,能有效实现井下弱特征人员重识别。The low light,strong light disturbance,high dust and other environmental conditions underground in coal mines,as well as the similarity of clothing and coal falling on the face of underground personnel,make it difficult to re identify underground personnel with weak features.The existing personnel re identification methods only extract global features and do not fully consider local features,resulting in low accuracy of underground personnel re identification.In order to solve the above problems,a local feature guided label smoothing and optimization method for re-identification of underground personnel with weak features is proposed.This method first extracts global and local features of underground personnel images through convolutional neural networks.Secondly,the k-nearest neighbor similarity is used to calculate the complementarity score between global and local features,in order to measure the degree of similarity between global and local features.Finally,based on the score of feature complementarity,label smoothing is performed on local features and label optimization is performed on global features.The weight of each local feature is dynamically adjusted to improve the label of each local feature.The prediction results of local features are summarized.The more reliable information is used to improve the label as a global feature label,thereby reducing image noise and enhancing feature identification capability.The experimental results show that the method outperforms mainstream personnel re identification methods in terms of mean average precision(mAP),rank-1 accuracy(Rank-1),and mean inverse negative penalty(mINP)on both publicly available datasets and self built datasets containing images of underground personnel.It has good generalization and robustness,and can effectively achieve underground weak feature personnel re identification.

关 键 词：人员重识别 弱特征人员 局部特征 标签平滑 标签优化 特征互补性 

分 类 号：TD672[矿业工程—矿山机电]