The detection accuracy is not high due to the interference of binocular vision coupling in high-resolution 3D lidar imaging target detection. A method of high-resolution 3D lidar imaging target detection based on deep learning is proposed. The vision sensor and inertial sensor are used to collect high-resolution 3D lidar images, and the method of laser point cloud feature extraction and dynamic scene image fusion is used to synthesize high-resolution 3D lidar. The compensation filtering method is used to correct the point cloud distortion in the process of 3D lidar imaging target detection. The depth feature matching model of lidar imaging target detection is established through the map feature matching and depth learning model of local sliding window, and the high-resolution 3D lidar imaging target detection is realized according to the feature matching results and the lidar frame matching results. The simulation test results show that the laser radar imaging using this method for target detection has a minimum peak signal-to-noise ratio of 41 dB under noise interference, and the detection accuracy reaches 0.96.