PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling
Owing to the small size of the defect pixel area and poor defect-background contrast issues in industrial images, noise and missed detection can easily occur. Therefore, automated defect detection is both necessary and challenging. To address these issues, with parallel attention mechanism (PAM) and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10360803/ |
| _version_ | 1827398966976708608 |
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| author | Tingting Sui Junwen Wang |
| author_facet | Tingting Sui Junwen Wang |
| author_sort | Tingting Sui |
| collection | DOAJ |
| description | Owing to the small size of the defect pixel area and poor defect-background contrast issues in industrial images, noise and missed detection can easily occur. Therefore, automated defect detection is both necessary and challenging. To address these issues, with parallel attention mechanism (PAM) and dual-channel spatial pyramid pooling-fast block (DC_SPPF), a novel defect detection network, namely, PDDD-Net, is proposed in this paper. First, to make the detection network emphasize small defect areas better, the PAM block is proposed to be embedded into YOLOv5 to obtain more low-level visual features and improve the detection accuracy of microdefects. Meanwhile, by fusing multi-channel features, the DC_SPPF block is proposed to replace the raw spatial pyramid pooling-fast block to acquire richer discriminative features of the defect areas. Finally, The soft non-maximum suppression (Soft-NMS) module is used to undertake the feature candidate box filtering task in YOLOv5 to reduce missed detection. Two public datasets are adopted for the model evaluation: the Tianchi aluminum profile defects dataset (APDDD) and the power line insulator dataset (CPLID). The experimental results indicate that the proposed PDDD-Net network exhibits remarkable defect detection performance compared with other related detection methods. |
| first_indexed | 2024-03-08T19:37:30Z |
| format | Article |
| id | doaj.art-97176822e2794da79a8ccebe72034e11 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-97176822e2794da79a8ccebe72034e112023-12-26T00:07:31ZengIEEEIEEE Access2169-35362023-01-011114176414177510.1109/ACCESS.2023.334356610360803PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid PoolingTingting Sui0https://orcid.org/0000-0003-3230-7181Junwen Wang1https://orcid.org/0009-0001-5804-5952School of Electronic Information Engineering, Shanghai Dianji University, Shanghai, ChinaSchool of Electronic Information Engineering, Shanghai Dianji University, Shanghai, ChinaOwing to the small size of the defect pixel area and poor defect-background contrast issues in industrial images, noise and missed detection can easily occur. Therefore, automated defect detection is both necessary and challenging. To address these issues, with parallel attention mechanism (PAM) and dual-channel spatial pyramid pooling-fast block (DC_SPPF), a novel defect detection network, namely, PDDD-Net, is proposed in this paper. First, to make the detection network emphasize small defect areas better, the PAM block is proposed to be embedded into YOLOv5 to obtain more low-level visual features and improve the detection accuracy of microdefects. Meanwhile, by fusing multi-channel features, the DC_SPPF block is proposed to replace the raw spatial pyramid pooling-fast block to acquire richer discriminative features of the defect areas. Finally, The soft non-maximum suppression (Soft-NMS) module is used to undertake the feature candidate box filtering task in YOLOv5 to reduce missed detection. Two public datasets are adopted for the model evaluation: the Tianchi aluminum profile defects dataset (APDDD) and the power line insulator dataset (CPLID). The experimental results indicate that the proposed PDDD-Net network exhibits remarkable defect detection performance compared with other related detection methods.https://ieeexplore.ieee.org/document/10360803/Defect detectionparallel attention mechanismspatial pyramid pooling-fastdeep learningattention fusion |
| spellingShingle | Tingting Sui Junwen Wang PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling IEEE Access Defect detection parallel attention mechanism spatial pyramid pooling-fast deep learning attention fusion |
| title | PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling |
| title_full | PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling |
| title_fullStr | PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling |
| title_full_unstemmed | PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling |
| title_short | PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling |
| title_sort | pddd net defect detection network based on parallel attention mechanism and dual channel spatial pyramid pooling |
| topic | Defect detection parallel attention mechanism spatial pyramid pooling-fast deep learning attention fusion |
| url | https://ieeexplore.ieee.org/document/10360803/ |
| work_keys_str_mv | AT tingtingsui pdddnetdefectdetectionnetworkbasedonparallelattentionmechanismanddualchannelspatialpyramidpooling AT junwenwang pdddnetdefectdetectionnetworkbasedonparallelattentionmechanismanddualchannelspatialpyramidpooling |