@Article{cmc.2026.085761,
AUTHOR = {Jinlin Chen, Yiquan Wu},
TITLE = {Integrating Texture Attention and Task Guidance for Waterline Keypoint Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27864},
ISSN = {1546-2226},
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% <mml:math id="mml-ieqn-1"><mml:msub><mml:mi>mAP</mml:mi><mml:mrow><mml:mn>75</mml:mn></mml:mrow></mml:msub></mml:math> 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.},
DOI = {10.32604/cmc.2026.085761}
}