煤矿运输系统多元异常图像检测研究  

Research on multivariate abnormal image detection in coal mine transportation system

作　　者：吕东翰 胡而已 黄一珀 李汶璋 LYU Donghan;HU Eryi;HUANG Yipo;LI Wenzhang(Beijing Waytous Technologies Co.,Ltd.,Beijing 100190,China;Information Institute,Ministry of Emergency Management,Beijing 100029,China;School of Artificial Intelligence,Xi'an University of Electronic Science and Technology,Xi'an 710000,China;Beijing New Coal Mine Technologies Co.,Ltd.,Beijing 100029,China)

机构地区：[1]中科慧拓(北京)科技有限公司,北京100190 [2]应急管理部信息研究院,北京100029 [3]西安电子科技大学人工智能学院,陕西西安710000 [4]北京纽科曼科技有限公司,北京100029

出　　处：《工矿自动化》2024年第6期70-78,共9页Journal Of Mine Automation

基　　金：国家自然科学基金资助项目(52274159,52374165)。

摘　　要：煤矿运输系统的异常险情种类繁多、场景多样,煤矿现场异常事故的发生具有偶然性,异常样本的获取其数量远小于正常样本,造成正负样本不平衡。针对上述问题,提出一种基于超球重构数据描述(HRDD)的煤矿运输系统多元异常图像检测方法。在全卷积数据描述(FCDD)基础上引入图像重构辅助任务,选用均方差损失函数作为图像重构辅助任务的目标函数,将异常图像检测和定位量化为一个不等式约束优化问题。采用无缝融合技术将辅助数据集、异常样本融合到正常样本中,以缩小异常融合样本与正常样本的差异,扩大异常样本总量,平衡异常样本、正常样本的比例。通过多组噪声模拟实验和现场实验证明,以一定概率在抵抗区添加高斯噪声进行增强训练,可提高HRDD模型的抗噪效能、泛化能力、检测准确率等。消融实验结果表明:辅助数据集有效地改善了样本不平衡问题,准确率提高了36.5%;引入图像重构辅助任务可保证深层特征能够准确映射到异常位置,交并比(IoU)提升了33.4%;辅助数据集与图像重构辅助任务之间存在强耦合作用,二者组合使用能进一步激发HRDD算法的性能潜力;添加无缝融合样本、高斯噪声增强等在一定程度上提高了HRDD模型的泛化能力。对照实验结果表明,HRDD算法准确率及IoU均优于其他主流算法,相比FCDD算法,HRDD算法准确率、IoU分别提高了4.6%,7.0%,更适用于煤矿现场。There are various types and scenarios of abnormal risks in coal mine transportation systems.The occurrence of abnormal accidents at coal mine sites is accidental,and the number of abnormal samples obtained is much smaller than that of normal samples,resulting in an imbalance of positive and negative samples.In order to solve the above problems,a multivariate abnormal image detection method for coal mine transportation systems based on hypersphere reconstructed data description(HRDD)is proposed.On the basis of full convolutional data description(FCDD),an image reconstruction auxiliary task is introduced.The mean square error loss function is selected as the objective function of the image reconstruction auxiliary task.Abnormal image detection and positioning are quantified as an inequality constrained optimization problem.The seamless fusion technology is used to fuse auxiliary datasets and abnormal samples into normal samples,in order to reduce the difference between abnormal fusion samples and normal samples,expand the total number of abnormal samples,and balance the proportion of abnormal and normal samples.Through multiple sets of noise simulation experiments and onsite experiments,it has been proven that adding Gaussian noise to the resistance zone with a certain probability for enhanced training can improve the noise resistance efficiency,generalization capability,detection accuracy,and other aspects of the HRDD model.The results of the ablation experiment show that the auxiliary dataset effectively improves the problem of sample imbalance,with an accuracy increase of 36.5%.The introduction of image reconstruction auxiliary tasks can ensure that deep features can be accurately mapped to abnormal positions,resulting in an IoU improvement of 33.4%.There is a strong coupling effect between the auxiliary dataset and the image reconstruction auxiliary task.The combination of the two can further stimulate the performance potential of the HRDD algorithm.The addition of seamless fusion samples and Gaussian noise enh

关 键 词：煤矿运输系统 异常图像检测 全卷积数据描述 超球重构数据描述 HRDD 图像重构 无缝融合技术 高斯噪声增强 

分 类 号：TD528[矿业工程—矿山机电]