@inproceedings{1f6bd56c3c50499fad8a0ac525d11cbf,
title = "TGNet: Texture-Guided Network for Strip Steel Surface Defect Detection",
abstract = "Strip steel surface defect segmentation is a challenging task due to the high visual similarity between defect regions and the surrounding normal surface. Motivated by the sensitivity of texture features to subtle visual differences, as well as the success of camouflaged object detection (COD) in distinguishing visually similar patterns, this study explores the effectiveness of optimizing a deep model with texture features and proposes a texture-guided COD network (TGNet) for strip steel surface defect segmentation. In which a target texture learning module is used to guide the model to learn the texture features of the camouflaged object. A texture guiding module integrates target texture features and features extracted by the feature extractor. An adjacent fusion module integrates features from adjacent layers. The experimental results obtained on the four datasets demonstrate that the texture-guided method outperformed the other state-of-the-art methods for both strip steel surface defect segmentation and naturally camouflaged object detection.",
keywords = "camouflaged object detection, deep learning, defect segmentation, strip steel, texture feature",
author = "Sheng Gao and Honghao Wang and Lianlei Lin and Jiawei Wang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 16th IEEE Reliability and Prognostics and Health Management Conference, PHM-Xian 2025 ; Conference date: 10-10-2025 Through 12-10-2025",
year = "2025",
doi = "10.1109/PHM-Xian66756.2025.11427629",
language = "英语",
series = "2025 Global Reliability and Prognostics and Health Management Conference, PHM-Xian 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Huimin Wang and Steven Li",
booktitle = "2025 Global Reliability and Prognostics and Health Management Conference, PHM-Xian 2025",
address = "美国",
}