Automated fabric defect detection - A review

By Henry Y. T. Ngan, Grantham Kwok-Hung Pang, Nelson Hong Ching Yung. Image and Vision Computing, 2011

543 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/0a0d53ed428b5c28e7f2712e2d6b4e3123cfc668

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hichem Snoussi
2. Hao Dong
3. Shihao Tang
4. Shaobo Li
5. Jing Yang
6. Yang Chen
7. Jun Wang
8. Meina Qiao

In Industrial and Manufacturing Engineering

1. Hichem Snoussi
2. Yang Chen
3. Meina Qiao
4. Tian Wang

Citations in the corpus

• A fast and robust convolutional neural network-based defect detection model in product quality control (2017, methodology): “For fabrics defect detection [4], most algorithms employed Gabor filters and Gabor wavelet filters [7–9].”
• Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, background): “For fabric defect detection, scholars [13,14] reviewed the application and development of defect-detection methods commonly used in the production of textile fabrics from the perspective of defect-detection development of the textile industry production.”