1. First Research Institute of the Ministry of Public Security of PRC, Beijing 100048, China 2. School of Information Engineering, Minzu University of China, Beijing 100081, China 3. Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China 4. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. We propose a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. Specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy, YOLOv4 is transformed into a four-scale detection method. To improve the detection speed, model pruning is applied to the new model. By virtue of the improved detection methods, the robot can collect garbage autonomously. The detection speed is up to 66.67 frames/s with a mean average precision (mAP) of 95.099%, and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.
