  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.
