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Research and Application of U\(^2\)-NetP Network Incorporating Coordinate Attention for Ship Draft Reading in Complex Situations

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Abstract

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\(^\text {2}\)-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.

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Data Availability

Our dataset is provided by a company and cannot be accessed for free, but for the development and progress of the field, after many discussions with the company, we are only allowed to publish a small number of sample images. (https://github.com/lwh104/draft-reading).

Code Availability

Related code is available as open source.

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Funding

The research project of this paper is supported by the fund of National Natural Science Foundation of China(62276032), Method Study on the Grooming Behavior Intelligent Identification of Tephritid fly via Integrating Occurrence Domain of Action and Spatiotemporal Characteristics of Behavior; the China University Industry-University-Research Innovation Fund, the New Generation Information Technology Innovation Project 2020, the research and development of artificial intelligence insect grooming behavior automatic detection and statistical system – Taking Bactrocera minax as an example. (2020ITA03012).

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Authors and Affiliations

  1. School of Computer Science, Yangtze University, Jingzhou District, Jingzhou, 434023, Hubei, China

    Weihao Li, Wei Zhan, Tao Han, Peiwen Wang, Hu Liu, Mengyuan Xiong & Shengbing Hong

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  1. Weihao Li
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Correspondence to Wei Zhan.

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Li, W., Zhan, W., Han, T. et al. Research and Application of U\(^2\)-NetP Network Incorporating Coordinate Attention for Ship Draft Reading in Complex Situations. J Sign Process Syst 95, 177–195 (2023). https://doi.org/10.1007/s11265-022-01816-w

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