Real-time Object Detection Algorithm For Underwater Robots

Abstract

The marine aquaculture fishing industry has caused many problems such as low work efficiency and hidden safety hazards due to low automation. Research on marine fishing robots that can replace humans for autonomous operations has excellent significance and prospects. Aiming at the problems of uneven lighting, poor visibility, and many magazines in the underwater environment, the underwater fishing robot can quickly and autonomously detect marine products and other targets. This paper implements an underwater image enhancement algorithm that can be deployed on a small AI system and an object detection algorithm based on YOLOv5 (You Only Look Once Version 5). The algorithm includes using the limited contrast adaptive histogram equalization image enhancement technology to solve the image quality problems of blue-green and blurry underwater images and then using the YOLOv5 object detection network to detect and locate underwater creatures. Experimental results show that the algorithm can effectively solve poor underwater image quality and unclear targets and rapidly detect seafood targets. The detection accuracy of this algorithm can reach 85%. It has been applied to the underwater fishing robot independently developed by the team and deployed on the Jetson Xavier NX small AI system. The detection accuracy can reach 80%, and the detection speed can reach 30FPS.