JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Defect Detection Algorithm of Denim Fabric Based on Cascading Feature Extraction Architecture |
| 영문제목(English Title) |
A Defect Detection Algorithm of Denim Fabric Based on Cascading Feature Extraction Architecture |
| 저자(Author) |
Shuangbao Ma
Renchao Zhang
Yujie Dong
Yuhui Feng
Guoqin Zhang
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| 원문수록처(Citation) |
VOL 19 NO. 01 PP. 0109 ~ 0117 (2023. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1–3 percentage points. |
| 키워드(Keyword) |
Cascading Feature Extraction Architecture
Denim Defect Detection
ImageNet
Robustness
Transfer Learning
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| 파일첨부 |
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