文献类型：期刊文献

英文题名：Object Detection and Monocular Stable Distance Estimation for Road Environments: A Fusion Architecture Using YOLO-RedeCa and Abnormal Jumping Change Filter

作者：Lv, Hejun[1];Du, Yu[1];Ma, Yan[1];Yuan, Ying[1]

第一作者：Lv, Hejun

通讯作者：Du, Y[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;[1141739]北京联合大学机器人学院;

年份：2024

卷号：13

期号：15

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85200968729);WOS:【SCI-EXPANDED(收录号:WOS:001286921700001)】；

基金：This work is mainly supported by the Vehicle-road Cooperative Autonomous Driving Fusion Control Project, and sponsored by the Academic Research Projects of Beijing Union University (Nos. ZK80202003, ZK90202105), the Beijing Municipal Education Commission Science and Technology Program (Nos. KM202111417007, KM202211417006).

语种：英文

外文关键词：automatic driving technique; object detection; monocular distance measurement; Kalman filter

摘要：Enabling rapid and accurate comprehensive environmental perception for vehicles poses a major challenge. Object detection and monocular distance estimation are the two main technologies, though they are often used separately. Thus, it is necessary to strengthen and optimize the interaction between them. Vehicle motion or object occlusions can cause sudden variations in the positions or sizes of detection boxes within temporal data, leading to fluctuations in distance estimates. So, we propose a method to integrate a detector based on YOLOv5-RedeCa, a Bot-Sort tracker and an anomaly jumping change filter. This combination allows for more accurate detection and tracking of objects. The anomaly jump filter smooths distance variations caused by sudden changes in detection box sizes. Our method increases accuracy while reducing computational demands, showing outstanding performance on several datasets. Notably, on the KITTI dataset, the standard deviation of the continuous ranging results remains consistently low, especially in scenarios with multiple object occlusions or disappearances. These results validate our method's effectiveness and precision in managing dual tasks.