@inproceedings{aa015ff44fd24d15a13d4c36d8a4b50b,
title = "IFY-Net: Detecting Surface Defects in Real-Time on Intricate Steel Samples",
abstract = "Metal materials are widely used in the manufacturing industry. So surface defect detection of metal products is crucial. However, the problem of intricate defect samples exists in complex industrial environments. In this study, we propose Imbalance-Focused YOLO network (IFY-Net) algorithm, offering a novel solution to this issue. First, the network's receptive field is enhanced to improve the detection accuracy of scale-imbalanced samples. Furthermore, the receptive field enhancement algorithm is improved to ensure that the network focuses on scale-abnormal samples while minimizing the loss of local features. Lastly, a self-attention mechanism is introduced to improve the network's ability to identify background-similar samples. The proposed algorithm demonstrates a 4.5\% improvement in mean Average Precision (mAP) on GC10-DET dataset, while achieving a detection speed of 159.96 FPS. The experimental results validate the algorithm's effectiveness and real-time capability.",
keywords = "defect detection, intricate defect samples, receptive field, self-attention mechanism, YOLO11",
author = "Shuming Zhang and Xiaohan He and Yinchi Li and Meiling Wang and Zhenguo Liu",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 37th Chinese Control and Decision Conference, CCDC 2025 ; Conference date: 16-05-2025 Through 19-05-2025",
year = "2025",
doi = "10.1109/CCDC65474.2025.11090562",
language = "English",
series = "Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "172--177",
booktitle = "Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025",
address = "United States",
}