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- Title
基于深度学习的织物疵点检测研究进展.
- Authors
贺智明; 彭亚楠
- Abstract
In recent years, deep learning has achieved great success in image processing, target detection and other fields, providing a new method for fabric defect detection. The commonly used fabric defect detection methods were summarized, which are mainly divided into structure-based method, spectrum-based analysis method, statics-based method, model-based method and learning-based method. The principles of these methods were summarized and compared, and their advantages and disadvantages were analyzed. Then, the main methods and development status of fabric defect detection based on deep learning technology in recent years were described, and the future research direction in this field was analyzed, providing valuable academic reference for relevant researchers.
- Subjects
IMAGE processing; DEEP learning; TEXTILES; SUCCESS; TECHNOLOGY
- Publication
Wool Textile Journal, 2019, Vol 47, Issue 8, p83
- ISSN
1003-1456
- Publication type
Article
- DOI
10.19333/j.mfkj.2019030160806