A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection
Aluminum is extensively utilized in the aerospace, aviation, automotive, and other industries. The presence of surface defects on aluminum has a significant impact on product quality. However, traditional detection methods fail to meet the efficiency and accuracy requirements of industrial practices...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7691 |
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| author | Yizhe Li Yidong Xie Hu He |
| author_facet | Yizhe Li Yidong Xie Hu He |
| author_sort | Yizhe Li |
| collection | DOAJ |
| description | Aluminum is extensively utilized in the aerospace, aviation, automotive, and other industries. The presence of surface defects on aluminum has a significant impact on product quality. However, traditional detection methods fail to meet the efficiency and accuracy requirements of industrial practices. In this study, we propose an innovative aluminum surface defect detection method based on an optimized two-stage Faster R-CNN network. A 2D camera serves as the image sensor, capturing high-resolution images in real time. Optimized lighting and focus ensure that defect features are clearly visible. After preprocessing, the images are fed into a deep learning network incorporated with a multi-scale feature pyramid structure, which effectively enhances defect recognition accuracy by integrating high-level semantic information with location details. Additionally, we introduced an optimized Convolutional Block Attention Module (CBAM) to further enhance network efficiency. Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. The network was trained on a dataset of 3200 images captured by the image sensor, split in an 8:1:1 ratio for training, validation, and testing, respectively. The experimental results show a mean Average Precision (mAP) of 94.25%, with individual Average Precision (AP) values exceeding 80%, meeting industrial standards for defect detection. |
| format | Article |
| id | doaj-art-e935c8e2261944149b244ce90ede39b3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e935c8e2261944149b244ce90ede39b32024-12-13T16:32:27ZengMDPI AGSensors1424-82202024-11-012423769110.3390/s24237691A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect DetectionYizhe Li0Yidong Xie1Hu He2State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaState Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaState Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaAluminum is extensively utilized in the aerospace, aviation, automotive, and other industries. The presence of surface defects on aluminum has a significant impact on product quality. However, traditional detection methods fail to meet the efficiency and accuracy requirements of industrial practices. In this study, we propose an innovative aluminum surface defect detection method based on an optimized two-stage Faster R-CNN network. A 2D camera serves as the image sensor, capturing high-resolution images in real time. Optimized lighting and focus ensure that defect features are clearly visible. After preprocessing, the images are fed into a deep learning network incorporated with a multi-scale feature pyramid structure, which effectively enhances defect recognition accuracy by integrating high-level semantic information with location details. Additionally, we introduced an optimized Convolutional Block Attention Module (CBAM) to further enhance network efficiency. Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. The network was trained on a dataset of 3200 images captured by the image sensor, split in an 8:1:1 ratio for training, validation, and testing, respectively. The experimental results show a mean Average Precision (mAP) of 94.25%, with individual Average Precision (AP) values exceeding 80%, meeting industrial standards for defect detection.https://www.mdpi.com/1424-8220/24/23/7691image sensorsdeep learning networkdefect detection |
| spellingShingle | Yizhe Li Yidong Xie Hu He A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection Sensors image sensors deep learning network defect detection |
| title | A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection |
| title_full | A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection |
| title_fullStr | A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection |
| title_full_unstemmed | A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection |
| title_short | A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection |
| title_sort | lightweight deep learning network with an optimized attention module for aluminum surface defect detection |
| topic | image sensors deep learning network defect detection |
| url | https://www.mdpi.com/1424-8220/24/23/7691 |
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