EC-PFN: a multiscale woven fusion network for industrial product surface defect detection
Abstract In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion Network (EC-PFN). The network employs a Featur Weav...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01699-3 |
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| author | Shuangning Liu Junfeng Li |
| author_facet | Shuangning Liu Junfeng Li |
| author_sort | Shuangning Liu |
| collection | DOAJ |
| description | Abstract In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion Network (EC-PFN). The network employs a Featur Weave Network architecture to enhance contextal awareess and parallel fusion capabilities. It utilizes a Feature Fusion Module (UniFusionLayer) for effective multiscale and multisemantic feature learning, offering new perspectives on feature fusion. Additionally, a Receptive Field Block (RFB) module is introduced to expand the receptive field, enhancing feature extraction in scenarios with low contrast and subtle defects. The Loss Ranking Module (LRM) is incorporated to optimize the target-oriented loss, improving performance by omitting low-confidence bounding boxes. Extensive experiments on a light guide plate defect dataset demonstrate that EC-PFN achieves a detection accuracy (mAP) of 98.9%, a detection speed of 92 FPS, and a computational cost of 14.5 GFLOPs, outperforming mainstream surface defect detection models. |
| format | Article |
| id | doaj-art-5b10a0bce210425082aa0b50549a4f7d |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-5b10a0bce210425082aa0b50549a4f7d2025-02-02T12:49:14ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111910.1007/s40747-024-01699-3EC-PFN: a multiscale woven fusion network for industrial product surface defect detectionShuangning Liu0Junfeng Li1The school of Information Science and Engineering, Zhejiang Sci-Tech UniversityThe school of Information Science and Engineering, Zhejiang Sci-Tech UniversityAbstract In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion Network (EC-PFN). The network employs a Featur Weave Network architecture to enhance contextal awareess and parallel fusion capabilities. It utilizes a Feature Fusion Module (UniFusionLayer) for effective multiscale and multisemantic feature learning, offering new perspectives on feature fusion. Additionally, a Receptive Field Block (RFB) module is introduced to expand the receptive field, enhancing feature extraction in scenarios with low contrast and subtle defects. The Loss Ranking Module (LRM) is incorporated to optimize the target-oriented loss, improving performance by omitting low-confidence bounding boxes. Extensive experiments on a light guide plate defect dataset demonstrate that EC-PFN achieves a detection accuracy (mAP) of 98.9%, a detection speed of 92 FPS, and a computational cost of 14.5 GFLOPs, outperforming mainstream surface defect detection models.https://doi.org/10.1007/s40747-024-01699-3Surface defect detectionIndustrial productHard sample miningWoven fusion networkDeep learning |
| spellingShingle | Shuangning Liu Junfeng Li EC-PFN: a multiscale woven fusion network for industrial product surface defect detection Complex & Intelligent Systems Surface defect detection Industrial product Hard sample mining Woven fusion network Deep learning |
| title | EC-PFN: a multiscale woven fusion network for industrial product surface defect detection |
| title_full | EC-PFN: a multiscale woven fusion network for industrial product surface defect detection |
| title_fullStr | EC-PFN: a multiscale woven fusion network for industrial product surface defect detection |
| title_full_unstemmed | EC-PFN: a multiscale woven fusion network for industrial product surface defect detection |
| title_short | EC-PFN: a multiscale woven fusion network for industrial product surface defect detection |
| title_sort | ec pfn a multiscale woven fusion network for industrial product surface defect detection |
| topic | Surface defect detection Industrial product Hard sample mining Woven fusion network Deep learning |
| url | https://doi.org/10.1007/s40747-024-01699-3 |
| work_keys_str_mv | AT shuangningliu ecpfnamultiscalewovenfusionnetworkforindustrialproductsurfacedefectdetection AT junfengli ecpfnamultiscalewovenfusionnetworkforindustrialproductsurfacedefectdetection |