NanoDet Model-Based Tracking and Inspection of Net Cage Using ROV

Abstract

Open sea cage culture has become a major trend in mariculture, with strong wind resistance, wave resistance, anti-current ability, high degree of intensification, breeding density, and high yield. However, damage to the cage triggers severe economic losses; hence, to adopt effective and timely measures in minimizing economic losses, it is crucial for farmers to identify and understand the damage to the cage without delay. Presently, the damage detection of nets is mainly achieved by the underwater operation of divers, which is highly risky, inefficient, expensive, and exhibits poor real-time performance. Here, a remote-operated vehicle (ROV)-based autonomous net detection method is proposed. The system comprises two parts: the first part is sonar image target detection based on NanoDet. The sonar constantly collects data in the front and middle parts of the ROV, and the trained NanoDet model is embedded into the ROV control end, with the actual output of the angle and distance information between the ROV and net. The second part is the control part of the robot. The ROV tracks the net coat based on the angle and distance information of the target detection. In addition, when there are obstacles in front of the ROV, or it is far away from the net, the D-STAR algorithm is adopted to realize local path planning. Experimental results indicate that the NanoDet target detection exhibits an average accuracy of 77.2% and a speed of approximately 10 fps, which satisfies the requirements of ROV tracking accuracy and speed. The average tracking error of ROV inspection is less than 0.5 m. The system addresses the problem of high risk and low efficiency of the manual detection of net damage in large-scale marine cage culture and can further analyze and predict the images and videos returned from the net. https://youtu.be/NKcgPcej5sI.