Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network
Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these prob...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7914 |
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| author | Jian Song Yingzhong Tian Xiang Wan |
| author_facet | Jian Song Yingzhong Tian Xiang Wan |
| author_sort | Jian Song |
| collection | DOAJ |
| description | Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects. Dynamic weights are assigned to achieve decision-level fusion through the use of a matrix of indicators to evaluate the performance of each channel’s recognition ability. In order to improve the detection efficiency and reduce the amount of data transmission and processing, an improved ROI detection algorithm for surface defects is proposed. It can enable the rapid screening of target surfaces for the high-quality and rapid acquisition of surface defect images. On an automotive pipeline surface defect dataset, the detection accuracy of the multi-channel fusion decision network with transfer learning was 97.78% and its detection speed was 153.8 FPS. The experimental results indicate that the multi-channel fusion decision network could simultaneously take into account the needs for real-time detection and accuracy, synthesize the advantages of different network structures, and avoid the limitations of single-channel networks. |
| format | Article |
| id | doaj-art-1a30d64b78504f4b956b07c0f0c9b016 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1a30d64b78504f4b956b07c0f0c9b0162024-12-27T14:52:25ZengMDPI AGSensors1424-82202024-12-012424791410.3390/s24247914Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 NetworkJian Song0Yingzhong Tian1Xiang Wan2Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaInstitute of Applied Physics, Jiangxi Academy of Sciences, Nanchang 330000, ChinaAlthough approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects. Dynamic weights are assigned to achieve decision-level fusion through the use of a matrix of indicators to evaluate the performance of each channel’s recognition ability. In order to improve the detection efficiency and reduce the amount of data transmission and processing, an improved ROI detection algorithm for surface defects is proposed. It can enable the rapid screening of target surfaces for the high-quality and rapid acquisition of surface defect images. On an automotive pipeline surface defect dataset, the detection accuracy of the multi-channel fusion decision network with transfer learning was 97.78% and its detection speed was 153.8 FPS. The experimental results indicate that the multi-channel fusion decision network could simultaneously take into account the needs for real-time detection and accuracy, synthesize the advantages of different network structures, and avoid the limitations of single-channel networks.https://www.mdpi.com/1424-8220/24/24/7914transfer learningfusion decision makingfast surface quality screeningsurface defect detection |
| spellingShingle | Jian Song Yingzhong Tian Xiang Wan Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network Sensors transfer learning fusion decision making fast surface quality screening surface defect detection |
| title | Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network |
| title_full | Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network |
| title_fullStr | Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network |
| title_full_unstemmed | Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network |
| title_short | Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network |
| title_sort | multi channel fusion decision making online detection network for surface defects in automotive pipelines based on transfer learning vgg16 network |
| topic | transfer learning fusion decision making fast surface quality screening surface defect detection |
| url | https://www.mdpi.com/1424-8220/24/24/7914 |
| work_keys_str_mv | AT jiansong multichannelfusiondecisionmakingonlinedetectionnetworkforsurfacedefectsinautomotivepipelinesbasedontransferlearningvgg16network AT yingzhongtian multichannelfusiondecisionmakingonlinedetectionnetworkforsurfacedefectsinautomotivepipelinesbasedontransferlearningvgg16network AT xiangwan multichannelfusiondecisionmakingonlinedetectionnetworkforsurfacedefectsinautomotivepipelinesbasedontransferlearningvgg16network |