Laser point cloud target detection based on deep learning has become an important research field. This article uses a SOTA deep learning network based on spherical projection and 2D images to achieve rapid detection of 3D laser point cloud targets. Firstly, a single frame 3D point cloud from the Semantic KITTI data set is transformed into a 2D RGB three channel image through spherical projection. The pixel position of the image plane depends on the three-dimensional coordinates of the point cloud, and the grayscale values of the R, G, and B channels depend on the normalized reflection intensity, distance, and height of the point cloud. Secondly, the overlapping distribution of spherical projections at different resolutions and their technical impact on image quality were analyzed. Finally, using the semantic segmentation model DeepLab-V3+network, simulation results show that this method has good performance in segmentation accuracy and speed, and has high application value.This paper presents a method of license plate character recognition based on the combination of Zernike moment and wavelet transformation features.