Long-Distance Vehicle Dynamic Detection and Positioning Based on Gm-APD Lidar and LIDAR-YOLO

Abstract

Aiming at the problems of long-distance night dynamic object detection in military operations, such as difficult imaging of traditional equipment, a small number of object pixels, a large difference in object size at different distances, and poor real-time performance. A long-distance vehicle detection method based on the LIDAR-YOLO network for Gm-APD (Geiger mode avalanche photodiode) lidar image is proposed. This method is improved based on YOLOv5s, which can detect long-distance vehicles and accurately locate motion trajectories in real-time. Firstly, the intensity image I of the lidar is preprocessed and combined with the distance image R, the BC_IIR image is obtained, and the vehicle detection data set is established. Then, according to the obvious vehicle features in the BC_IIR image, the low-complexity backbone network LIDARNet is redesigned, and several attention mechanism modules are added. The network feature extraction ability is enhanced by comparing and selecting the CA attention mechanism module. In the network prediction layer, context-aware feature map fusion is added to improve the detection precision of vehicles at different distances. Next, a GIOU loss is introduced to improve the detection precision of occluded vehicles. Finally, the method can depict the trajectory of the vehicle in 3D space based on distance information. After testing, the network achieves a detection speed of 62.67 frames per second (FPS), recall rate (R) = 98.19%, precision rate (P) = 95.53%, and F1 is 0.9675. The results show that the algorithm has better performance than other methods.