GCF-Net: Steel Surface Defect Detection Network Based on Global Attention Perception and Cross-Layer Interactive Fusion
Year: 2025
Type: article
Abstract: At present, the detection of steel surface defects is still challenging, because there are some problems in steel products, such as complex background and noise interference, making it difficult to accurately detect complex small targets and great changes in defects at different scales, which directly affects product quality and endangers life safety. To solve the above problems, this paper proposes a steel surface defect detection network based on global attention perception and cross-layer interactive fusion, named GCF-Net. Firstly, this paper proposes an Interactive Feature Extraction Network (IFE-Net), which uses a local modeling feature extraction module to enhance the extraction of local detail features and uses a global attention perception module to capture the global contextual information in the image, thus improving the detection of complex background and noise defects. Secondly, this paper proposes a Cross-Layer Interactive Fusion Network (CIF-Net), which makes up for the fine-grained information lost during the gradual refinement of features through the fusion of adjacent layers, fully integrates shallow and deep features, and at the same time enhances the interaction between different scales by cross-layer fusion, thus improving the recognition ability of defect targets of different scales. Thirdly, the Interactive Fusion Module (IFM) is proposed, which can adjust the importance of each mosaic feature by attention to make efficient use of all feature information and improve the detection of complex background defects. Finally, in order to solve the problems of difficult positioning and inaccurate detection of small targets, this paper aims to strengthen the sensitive loss Q_IOU of small targets and improve the perception of complex small targets in steel defects. Compared with the baseline model, [email protected] is improved by 7.0%, 4.4%, and 2.5% on the NEU-DET, PCB, and Steel datasets, respectively, and it is better than all of the comparison models.
Source: Electronics
Authors: Weipeng Shi, Changhe Li, Junlin Dai, Na Niu
Institution: Northeast Forestry University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409895079