Integrating Texture Attention and Task Guidance for Waterline Keypoint Detection
Jinlin Chen1,2, Yiquan Wu1,*
1 College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2 College of Finance and Mathematics, Huainan Normal University, Huainan, China
* Corresponding Author: Yiquan Wu. Email: 
(This article belongs to the Special Issue: Advanced Object Detection and Visual Understanding in Intelligent Systems)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.085761
Received 19 May 2026; Accepted 16 July 2026; Published online 10 August 2026
Abstract
Accurate waterline detection is critical for automated ship draft monitoring but remains challenging due to weak textures, low contrast, and dynamic maritime interferences. This paper presents TGNet, a task-guided framework that jointly optimizes character recognition and waterline keypoint localization. TGNet introduces a triple attention network (TAnet) with channel, spatial, and texture attention modules to enhance discriminative feature extraction. Crucially, a task-to-task guidance mechanism leverages detected draft characters to spatially constrain and crop feature maps, focusing the keypoint detection head on the most relevant waterline region. Extensive experiments on three large-scale aerial datasets show that TAnet consistently improves baseline detectors by an average of 2.8% recall and 3.6% mean average precision (mAP). On our self-built UAV ship draft dataset, TGNet achieves 92.3% recall and 93.8%
mAP75 at 56 FPS, outperforming state-of-the-art keypoint and segmentation methods while maintaining real-time efficiency. The proposed approach demonstrates a robust and efficient solution for practical autonomous draft reading.
Keywords
Ship draft measurement; waterline keypoint detection; task-guided deep learning; triple attention mechanism; UAV-based maritime vision; real-time ship monitoring
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