Safety incidents caused by pedestrians illegally intruding onto railway tracks occur frequently in rail transit scenes,sig-nificantly affecting the safe operation of trains.Utilizing single sensor data for pedestrian detection often leads to low recall rates of de-tection results and lack of category or orientation information in the results,which cannot meet practical field requirements.To address these issues,this paper proposed a pedestrian detection method based on the fusion of 3D point clouds and images in rail transit scenes.This method first employed a deep learning model trained on a constructed dataset of pedestrian data in rail transit scenes to detect pedes-trians separately in 3D point clouds and images.Subsequently,based on the principle of spatial position consistency of the targets in 3D point clouds and images,the rotation and translation matrix between the LiDAR and camera was solved.Finally,the 3D point cloud ob-ject detection results were projected onto the image coordinate system.To solve the issues of misalignment between multiple adjacent tar-gets and the one-to-many relationship between detection results,the intersection over union ratio and center point distance between the two detection results were calculated as fusion constraints,enabling more accurate pedestrian detection.Experimental results using data acquired from the field demonstrate that,compared to detection results from separate data of 3D point clouds and images,while maintain-ing timeliness,this method improves the recall rate by 4.5％and 5.5％,respectively,effectively reducing the risk of safety accidents caused by missed pedestrian detections,meeting the demand for pedestrian detection during actual train operations.
关键词
三维点云/图像检测/深度学习/多传感器融合/目标检测
Key words
3D point clouds/image detection/deep learning/multi-sensor fusion/object detection
维普
万方数据