Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection
This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network archi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7074 |
| _version_ | 1797511768258379776 |
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| author | Chao-Ching Ho Wei-Chi Chou Eugene Su |
| author_facet | Chao-Ching Ho Wei-Chi Chou Eugene Su |
| author_sort | Chao-Ching Ho |
| collection | DOAJ |
| description | This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects. |
| first_indexed | 2024-03-10T05:52:44Z |
| format | Article |
| id | doaj.art-3c446f383b9e478dbd9367daba10c873 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:44Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3c446f383b9e478dbd9367daba10c8732023-11-22T21:36:11ZengMDPI AGSensors1424-82202021-10-012121707410.3390/s21217074Deep Convolutional Neural Network Optimization for Defect Detection in Fabric InspectionChao-Ching Ho0Wei-Chi Chou1Eugene Su2Department of Mechanical Engineering, Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Mechanical Engineering, Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Mechanical Engineering, Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, TaiwanThis research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects.https://www.mdpi.com/1424-8220/21/21/7074deep convolutional neural networkdefect detectionmachine visionembedded inspectiondeep learning network optimizationpruning parameter |
| spellingShingle | Chao-Ching Ho Wei-Chi Chou Eugene Su Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection Sensors deep convolutional neural network defect detection machine vision embedded inspection deep learning network optimization pruning parameter |
| title | Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection |
| title_full | Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection |
| title_fullStr | Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection |
| title_full_unstemmed | Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection |
| title_short | Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection |
| title_sort | deep convolutional neural network optimization for defect detection in fabric inspection |
| topic | deep convolutional neural network defect detection machine vision embedded inspection deep learning network optimization pruning parameter |
| url | https://www.mdpi.com/1424-8220/21/21/7074 |
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