摘要
公路洒落物是影响交通安全的重要因素之一,为了解决中小尺度公路洒落物检测中的漏检、误检以及难以定位等问题,本文提出了一种图像引导和点云空间约束的公路洒落物检测定位方法。该方法使用改进的YOLOv7-OD网络处理图像数据获取二维目标预测框信息,将目标预测框投影到激光雷达坐标系下得到锥形感兴趣区域(region of interest,ROI)。在ROI区域内的点云空间约束下,联合点云聚类和点云生成算法获得不同尺度的洒落物在三维空间中的检测定位结果。实验表明:改进的YOLOv7-OD网络在中尺度目标上的召回率和平均精度分别为85.4%和82.0%,相比YOLOv7网络分别提升6.6和8.0个百分点;在小尺度目标上的召回率和平均精度分别为66.8%和57.3%,均提升5.3个百分点;洒落物定位方面,对于距离检测车辆30~40 m处的目标,深度定位误差为0.19 m,角度定位误差为0.082°,实现了多尺度公路洒落物的检测和定位。
Abandoned objects on the road significantly impact traffic safety.To address issues such as missed detections,false alarms,and localization difficulties encountered in detecting of small-to-medium-sized abandoned objects,this paper proposes a method for detecting and locating abandoned objects on the road using image guidance and point cloud spatial constraints.The method employs an improved YOLOv7-OD network to process image data,extracting information about two-dimensional target bounding boxes.Subsequently,these bounding boxes are projected onto the coordinate system of the LiDAR sensor to get a pyramidal region of interest(ROI).Under the spatial constraints of the point cloud within the ROI,the detection and localization results of abandoned objects on the road in three-dimensional space are obtained through a combination of point cloud clustering and point cloud generation algorithms.The experimental results show that the improved YOLOv7-OD network achieves recall and average precision rates of 85.4%and 82.0%,respectively,for medium-sized objects,representing an improvement of 6.6%and 8.0%compared to the YOLOv7.The recall and average precision rates for small-sized objects are 66.8%and 57.3%,respectively,with an increase of 5.3%.Regarding localization,for targets located 30-40 m away from the detecting vehicle,the depth localization error is 0.19 m,and the angular localization error is 0.082°,enabling the detection and localization of multi-scale abandoned objects on the road.
作者
蔡怀宇
杨朝乾
崔子扬
汪毅
陈晓冬
Cai Huaiyu;Yang Zhaoqian;Cui Ziyang;Wang Yi;Chen Xiaodong(School of Precision Instrument and Opto-electronics Engineering,Tianjin University,Tianjin 300072,China;Key Laboratory of Optoelectronic Information Technology Ministry of Education,Tianjin University,Tianjin 300072,China)
出处
《光电工程》
CAS
CSCD
北大核心
2024年第3期129-141,共13页
Opto-Electronic Engineering
关键词
公路洒落物
图像
激光雷达点云
目标检测
abandoned objects on the road
image
LiDAR point cloud
object detection
