TY - EJOU AU - Chen, Jinlin AU - Wu, Yiquan TI - Integrating Texture Attention and Task Guidance for Waterline Keypoint Detection T2 - Computers, Materials \& Continua PY - VL - IS - SN - 1546-2226 AB - 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. KW - Ship draft measurement; waterline keypoint detection; task-guided deep learning; triple attention mechanism; UAV-based maritime vision; real-time ship monitoring DO - 10.32604/cmc.2026.085761