3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds

2020-04-21 · Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU

Lidar based 3D object detection and classification tasks are essential for autonomous driving(AD). A lidar sensor can provide the 3D point cloud data reconstruction of the surrounding environment. However, real time detection in 3D point clouds still needs a strong algorithmic. This paper proposes a 3D object detection method based on point cloud and image which consists of there parts.(1)Lidar-camera calibration and undistorted image transformation. (2)YOLO-based detection and PointCloud extraction, (3)K-means based point cloud segmentation and detection experiment test and evaluation in depth image. In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we 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 can achieve High-speed 3D object recognition function in GPU. The accuracy and precision get imporved after k-means clustering in point cloud. The speed of our detection method is a advantage faster than PointNet.

📄 PDF Abstract BibTeX arXiv:2005.02132

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Tasks

2D Object Detection 3D Object Detection 3D Object Recognition Autonomous DrivingCamera CalibrationClusteringGPUObjectobject-detection Object Detection Object Recognition Point Cloud Segmentation

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