Research on visual tracking system of lightweight garbage collection robot

To increase the autonomous perception ability of the garbage pickup robot, an improved lightweight target detection algorithm YOLO-TrashNet based on YOLOV4 for garbage tracking vision system is proposed. Aiming at the trade-off between speed and accuracy of the visual tracking system, the backbone network is replacedwith MobileNetV3 on the basis of YOLOV4, the effects of SE (Squeeze-and-Excitation) attention mechanism, CBAM (Convolutional Block Attention Module) attention mechanism and CSP cross-level local network structure on the performance of the algorithm are analyzed.Ithasbuilt a garbage collection robot vision system, used Realsense depth cameras that can improve target positioning, collected 15 most common types of garbage in public places, and completed indoor garbage tracking experiments.The experimental results shows that the CSPMobileNetV3-CBAM backbone network model proposed in this paper can greatly increase the detection speed, compared with YOLO-V4, the amount of calculation is reduced by 93.3%, the weight is only 19.5MB, and the memory consumption is lower than YOLOV4-tiny. Compared with YOLO-V4, the Garbage detection sacrifices 4% accuracy and its speed is increased by 6 times on Jetson Nano, its mAP is 86.3%. Provides a high-real-time and high-accuracy visual tracking system for garbage collection robots.