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.
Li et al. (Thu,) studied this question.