YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud
This addresses real-time 3D object detection for automated driving, but it is incremental as it integrates existing methods.
The paper tackles 3D object detection for automated driving by combining YOLO-based 2D detection on camera images with k-means clustering on LiDAR point clouds, achieving high-speed recognition on GPU.
Lidar based 3D object detection and classification tasks are essential for automated driving(AD). A Lidar sensor can provide the 3D point coud data reconstruction of the surrounding environment. But the detection in 3D point cloud still needs a strong algorithmic challenge. This paper consists of three parts.(1)Lidar-camera calib. (2)YOLO, based detection and PointCloud extraction, (3) k-means based point cloud segmentation. In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar. By comparing whether 2D coordinate transferred from the 3D point is in the object bounding box or not, and doing a k-means clustering can achieve High-speed 3D object recognition function in GPU.