复杂矿山环境下作业车辆的实时检测与跟踪  

Real-time Detection and Tracking for Operating Vehicles in Complex Mining Environments

作　　者：康高强 林军 刘世望 岳伟 熊群芳 仝皓 KANG Gaoqiang;LIN Jun;LIU Shiwang;YUE Wei;XIONG Qunfang;TONG Hao(CRRC Zhuzhou Institute Co.,Ltd.,Zhuzhou,Hunan 412001,China)

机构地区：[1]中车株洲电力机车研究所有限公司,湖南株洲412001

出　　处：《控制与信息技术》2022年第5期68-74,共7页CONTROL AND INFORMATION TECHNOLOGY

基　　金：国家重点研发计划(2021YFB2501802)。

摘　　要：针对复杂矿山环境下作业车辆与背景图像相似而检测效果差、多类型车辆跟踪稳定性低等问题,文章提出了一种多类别、多目标的复杂矿山环境下作业车辆实时检测与跟踪算法。其基于轻量级骨干网络YOLO并结合多尺度特征融合模块构建模型框架,该模型以DIoU为损失函数,采用K-means聚类回归候选框尺寸,通过轻量级骨干网络学习图像特征,输出多尺度预测结果。在此基础上,将多类别作业车辆目标的特征作为相似性度量,结合表征运动信息的马氏距离度量和余弦度量进行级联匹配,并串联IoU匹配和卡尔曼滤波来确认轨迹,从而实现多作业车辆实时跟踪。实验结果显示,该算法的车辆检测平均准确率mAP@0.5-0.95为58.40%,多目标跟踪精度达到82.60%,每帧图像处理时间为26.5 ms,表明采用该算法能够有效进行作业车辆的实时检测与跟踪。Aiming at the problems of poor detection effect and low tracking stability of multi-type vehicles in complex mining environment due to the similarity of operating vehicles and background images, this paper proposes a multi-category and multi-target real-time detection and tracking algorithm for operating vehicles in complex mining environments. The model framework is constructed based on the lightweight backbone network YOLO combined with the multi-scale feature fusion module. The model uses DIoU as loss function, uses K-means clustering to regress the size of candidate frame, and learns image features through the lightweight backbone network. On this basis, the features of multi-category work vehicle targets are used as similarity metric, combined with Mahalanobis distance metric and cosine metric that characterize motion information for cascading matching, and IoU matching and Kalman filtering are connected in series to confirm the trajectory and the real-time tracking of multiple work vehicles. Experimental results show that the average vehicle detection accuracy of the algorithm mAP@0.5-0.95 is 58.40%, the multi-target tracking accuracy reaches 82.60%, and the image processing time per frame is 26.5 ms, which can effectively perform real-time detection and tracking of working vehicles.

关 键 词：无人驾驶 多作业车辆跟踪 车辆检测 级联匹配 YOLO算法 卡尔曼滤波 矿用卡车 

分 类 号：TD572[矿业工程—矿山机电]