Research and Application of U2-NetP Network Incorporating Coordinate Attention for Ship Draft Reading in Complex Situations

Ship draft reading is an essential link to the draft survey. At present, manual observation is primarily used to determine a ship’s draft. However, manual observation is easily affected by complex situations such as Large waves on the water, Water obstacles, Water traces, Tilted draft characters, and Rusted draft characters. Traditional image-based methods of ship draft reading are difficult to adapt to these complex situations, and existing deep learning-based methods have disadvantages such as the poor robustness of ship draft reading in various complex situations. In this paper, we proposed a method that combines image processing and deep learning and is capable of adapting to a variety of complex situations, particularly in the presence of Large waves on the water and Water obstacles. We also propose a small U 2 -NetP neural network for semantic segmentation that incorporates Coordinate attention, hence enhancing the capture of information regarding spatial locations. Furthermore, its segmentation accuracy reached 96.47% compared with the original network. In addition, in consideration of the combination of lightweight and multitasking of the method, we use the lightweight Yolov5n network architecture to detect the ship draft characters, which achieves 98% of mAP_0.5 and effectively improves the lightweight of the draft reading. Experimental results on a real dataset encompassing many difficult situations illustrate the state-of-the-art performance of the suggested reading approach when compared to other existing deep learning methods. The average inaccuracy of the draft reading is less than ±0.005 m, and millimeter-level precision is achievable. It can serve as a valuable resource for manual reading. In addition, our work lays the groundwork for future research on the deployment of edge devices.