Automatic Fabric Defect Detection Based on an Improved YOLOv5

Author:

Jin Rui12ORCID,Niu Qiang1ORCID

Affiliation:

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

2. Changzhou Vocational Institute of Textile and Garment, Changzhou, China

Abstract

Fabric defect detection is particularly remarkable because of the large textile production demand in China. Traditional manual detection method is inefficient, time-consuming, laborious, and costly. A deep learning technique is proposed in this work to perform automatic fabric defect detection by improving a YOLOv5 object detection algorithm. A teacher-student architecture is used to handle the shortage of fabric defect images. Specifically, a deep teacher network could precisely recognize fabric defects. After information distillation, a shallow student network could do the same thing in real-time with minimal performance degeneration. Moreover, multitask learning is introduced by simultaneously detecting ubiquitous and specific defects. Focal loss function and central constraints are introduced to improve the recognition performance. Evaluations are performed on the publicly available Tianchi AI and TILDA databases. Results indicate that the proposed method performs well compared with other methods and has excellent defect detection ability in the collected textile images.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Reference41 articles.

1. FDAS: a knowledge-based framework for analysis of defects in woven textile structures;K. Srinivasan;Journal of the Textile Institute Proceedings and Abstracts,1990

2. Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

3. Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

4. Automatic inspection of simply patterned material in the textile industry;T. Thomas

5. Fabric defect detection using fuzzy inductive reasoning based on image histogram statistic variables;Y. Ye

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