Localization and Pixel-Confidence Network for Surface Defect Segmentation
Surface defect segmentation based on deep learning has been widely applied in industrial inspection. However, two major challenges persist in specific application scenarios: first, the imbalanced area distribution between defects and the background leads to degraded segmentation performance; second,...
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2025-07-01
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| author | Yueyou Wang Zixuan Xu Li Mei Ruiqing Guo Jing Zhang Tingbo Zhang Hongqi Liu |
| author_facet | Yueyou Wang Zixuan Xu Li Mei Ruiqing Guo Jing Zhang Tingbo Zhang Hongqi Liu |
| author_sort | Yueyou Wang |
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| description | Surface defect segmentation based on deep learning has been widely applied in industrial inspection. However, two major challenges persist in specific application scenarios: first, the imbalanced area distribution between defects and the background leads to degraded segmentation performance; second, fine gaps within defects are prone to over-segmentation. To address these issues, this study proposes a two-stage image segmentation network that integrates a Defect Localization Module and a Pixel Confidence Module. In the first stage, the Defect Localization Module performs a coarse localization of defect regions and embeds the resulting feature vectors into the backbone of the second stage. In the second stage, the Pixel Confidence Module captures the probabilistic distribution of neighboring pixels, thereby refining the initial predictions. Experimental results demonstrate that the improved network achieves gains of <inline-formula><math display="inline"><semantics><mrow><mn>1.58</mn><mo>%</mo><mo>±</mo><mn>0.80</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>P</mi><mi>A</mi></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mn>1.35</mn><mo>%</mo><mo>±</mo><mn>0.77</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>I</mi><mi>o</mi><mi>U</mi></mrow></semantics></math></inline-formula> on the self-built Carbon Fabric Defect Dataset and <inline-formula><math display="inline"><semantics><mrow><mn>2.66</mn><mo>%</mo><mo>±</mo><mn>1.12</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>P</mi><mi>A</mi></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mn>1.44</mn><mo>%</mo><mo>±</mo><mn>0.79</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>I</mi><mi>o</mi><mi>U</mi></mrow></semantics></math></inline-formula> on the public Magnetic Tile Defect Dataset compared to the other network. These enhancements translate to more reliable automated quality assurance in industrial production environments. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-0bf3298212294dacb11fd1a0275a8faa2025-08-20T03:36:30ZengMDPI AGSensors1424-82202025-07-012515454810.3390/s25154548Localization and Pixel-Confidence Network for Surface Defect SegmentationYueyou Wang0Zixuan Xu1Li Mei2Ruiqing Guo3Jing Zhang4Tingbo Zhang5Hongqi Liu6Aerospace Research Institute of Materials and Processing Technology, Beijing 100076, ChinaHuazhong School of Mechanical Science and Engineering, University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, ChinaAerospace Research Institute of Materials and Processing Technology, Beijing 100076, ChinaAerospace Research Institute of Materials and Processing Technology, Beijing 100076, ChinaAerospace Research Institute of Materials and Processing Technology, Beijing 100076, ChinaAerospace Research Institute of Materials and Processing Technology, Beijing 100076, ChinaHuazhong School of Mechanical Science and Engineering, University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, ChinaSurface defect segmentation based on deep learning has been widely applied in industrial inspection. However, two major challenges persist in specific application scenarios: first, the imbalanced area distribution between defects and the background leads to degraded segmentation performance; second, fine gaps within defects are prone to over-segmentation. To address these issues, this study proposes a two-stage image segmentation network that integrates a Defect Localization Module and a Pixel Confidence Module. In the first stage, the Defect Localization Module performs a coarse localization of defect regions and embeds the resulting feature vectors into the backbone of the second stage. In the second stage, the Pixel Confidence Module captures the probabilistic distribution of neighboring pixels, thereby refining the initial predictions. Experimental results demonstrate that the improved network achieves gains of <inline-formula><math display="inline"><semantics><mrow><mn>1.58</mn><mo>%</mo><mo>±</mo><mn>0.80</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>P</mi><mi>A</mi></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mn>1.35</mn><mo>%</mo><mo>±</mo><mn>0.77</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>I</mi><mi>o</mi><mi>U</mi></mrow></semantics></math></inline-formula> on the self-built Carbon Fabric Defect Dataset and <inline-formula><math display="inline"><semantics><mrow><mn>2.66</mn><mo>%</mo><mo>±</mo><mn>1.12</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>P</mi><mi>A</mi></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mn>1.44</mn><mo>%</mo><mo>±</mo><mn>0.79</mn><mo>%</mo></mrow></semantics></math></inline-formula> in <inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>I</mi><mi>o</mi><mi>U</mi></mrow></semantics></math></inline-formula> on the public Magnetic Tile Defect Dataset compared to the other network. These enhancements translate to more reliable automated quality assurance in industrial production environments.https://www.mdpi.com/1424-8220/25/15/4548surface defect segmentationdeep learningmachine visiontwo-stage model |
| spellingShingle | Yueyou Wang Zixuan Xu Li Mei Ruiqing Guo Jing Zhang Tingbo Zhang Hongqi Liu Localization and Pixel-Confidence Network for Surface Defect Segmentation Sensors surface defect segmentation deep learning machine vision two-stage model |
| title | Localization and Pixel-Confidence Network for Surface Defect Segmentation |
| title_full | Localization and Pixel-Confidence Network for Surface Defect Segmentation |
| title_fullStr | Localization and Pixel-Confidence Network for Surface Defect Segmentation |
| title_full_unstemmed | Localization and Pixel-Confidence Network for Surface Defect Segmentation |
| title_short | Localization and Pixel-Confidence Network for Surface Defect Segmentation |
| title_sort | localization and pixel confidence network for surface defect segmentation |
| topic | surface defect segmentation deep learning machine vision two-stage model |
| url | https://www.mdpi.com/1424-8220/25/15/4548 |
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