A Defect Detection Algorithm of Denim Fabric Based on Cascading Feature Extraction Architecture

Shuangbao Ma; Renchao Zhang; Yujie Dong; Yuhui Feng; Guoqin Zhang

doi:10.3745/JIPS.04.0265

추천

검색

자료유형: 학술저널

저자정보: Shuangbao Ma (Wuhan Textile University) Renchao Zhang (Wuhan Textile University) Yujie Dong (Wuhan Textile University) Yuhui Feng (Wuhan Textile University) Guoqin Zhang (Wuhan Textile University)

저널정보: 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제19권 제1호

발행연도: 2023.2

수록면: 109 - 117 (9page)

DOI: 10.3745/JIPS.04.0265

이용수

📌

연구주제

📖

연구배경

🔬

연구방법

🏆

연구결과

초록· 키워드

오류제보하기

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denimfabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extractionarchitecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the largedataset ImageNet and uses its portability to train the defect detection classifier and the defect recognitionclassifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layerwere retrained and adjusted from of these two training models on the high-definition fabric defect dataset. Thelast 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 otherfeature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show thatthe defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increasedby 1–3 percentage points.

#Cascading Feature Extraction Architecture #Denim Defect Detection #ImageNet #Robustness #Transfer Learning

참고문헌 (0)

참고문헌 신청

참고문헌이 DBpia에서 서비스 중이라면, [참고문헌 신청]을 통해 등록해보세요

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!