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.
Highlights
Fabric defect detection is remarkable because of the large textile production demand in China
An ablation study is performed on the Tianchi AI database to verify the effects of different improvement methods, including multitask learning, focal loss, and central loss constraints. e results are presented in Table 1. e ablation study of the teacher network shows that the student network has similar results
Compared with the YOLOv5-based detection method, the introduced attention module could lead to an improved performance with increased area under the ROC curve (AUC) and mean average precision (mAP). en, AUC and mAP are further improved by simultaneously detecting ubiquitous and specific defects with the proposed multitask learning strategy because of the complementarity between different tasks
Summary
Fabric defect detection is remarkable because of the large textile production demand in China. Results indicate that the proposed method performs well compared with other methods and has excellent defect detection ability in the collected textile images. How to detect fabric defects by automatic means has become an engaging, difficult research spot in the field of textile industry and machine vision. E core of machine-vision-based fabric defect detection is extracting the characteristics related to defects from the textile images. Researchers proposed methods based on edges [6], local binary patterns [7, 8], contour waves [9], and gray co-occurrence matrix [10, 11]. Ese methods perform well in identifying defective images but have difficulty recognizing specific fabric defects.
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