YoloV8 Based Novel Approach for Object Detection on LiDAR Point Cloud

Object detection serves as an important perception task for both autonomous vehicles and Advanced Driver Assistance Systems (ADAS). While object detection in camera images has been extensively studied, tackling this task with Light Detection and Ranging (LiDAR) data presents unique challenges due to its inherent sparsity. This study introduces a pioneering approach for LiDAR-based object detection, wherein LiDAR point cloud data is ingeniously transformed into a pseudo-RGB image format, subsequently fed into the YOLOv8 network originally designed for camera-based object detection. Trained and rigorously evaluated on the KITTI dataset, our method demonstrates outstanding performance, achieving an impressive mean Average Precision (mAP) of more than 86%. The model was also tested on some point clouds from TiHAN IITH Autonomous Navigation Dataset (TIAND). This remarkable result highlights the efficacy of the proposed approach in harnessing LiDAR data for robust object detection, thus contributing to the advancement of perception capabilities in autonomous driving and ADAS applications.