Automatic Fabric Defect Detection Based on an Improved YOLOv5

• Main Authors: Rui Jin, Qiang Niu
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Mathematical Problems in Engineering
• Online Access: http://dx.doi.org/10.1155/2021/7321394

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.