Research of garbage salvage system based on deep learning

Abstract

The increase in surface trash salvage tasks has led to problems such as high staff workload, high labor costs, and low work efficiency. A garbage salvage system with autonomous cruising, identification, and detection is designed for this problem. This paper is based on deep learning technology to detect surface garbage and salvage garbage by robotic arm to solve the problem of low intelligence of surface garbage salvage as well as the problem caused by rising labor cost and insufficient labor, and to improve the efficiency of surface garbage salvage task. The system is equipped with a ROS robot operating system and uses lidar to acquire environmental information, realize map construction and autonomous cruise of the salvage vessel, use the YOLO v4 target detection model to detect garbage, and then apply the detected target and location information to the intelligent garbage salvage system. Five common types of garbage on the water surface are collected, and the average detection speed reaches 45 FPS and the average recognition accuracy is up to 88% through experimental verification, meeting the real-time system application.