Abstract
Net cleaning robots have been playing an increasingly important role in offshore aquaculture due to their efficiency and labor-saving capabilities. However, in practice, these robots are still entirely teleoperated and require constant, skilled human operation. The mesh intersection points, which serve as a structural feature of the nets, provide valuable visual cues for robot self-localization and net damage identification. Therefore, the detection and tracking of these points are crucial for developing autonomous net cleaning robots. To achieve intersection point detection, we propose NPUNet-lite, a lightweight model based on U-Net. This model significantly minimizes computational resources and model size while preserving high detection accuracy. For reliable point tracking, we develop the NlPTrack algorithm, which incorporates an iterative closest point-based association strategy to meet spatial constraints between points within a frame, and a cascaded association strategy to satisfy homographic and epipolar constraints across adjacent frames. We build a dataset from videos collected during a robotic cleaning task to train and evaluate our methods. The experimental results indicate that our segmentation network achieves comparable accuracy to advanced networks, yet with a substantial reduction in computational cost. Meanwhile, the tracking method successfully tracks the majority of intersection points across scenarios where the robot moves in different directions.