TY  - EJOU
AU  - Jiang, Jielin 
AU  - Cui, Chao 
AU  - Xu, Xiaolong 
AU  - Cui, Yan 

TI  - Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection
T2  - Intelligent Automation \& Soft Computing

PY  - 2024
VL  - 39
IS  - 4
SN  - 2326-005X

AB  - In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose an effective algorithm, namely multi-layer feature extraction combined with deformable convolution (MFDC), for fabric defect detection. In MFDC, multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects. On this basis, a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects. In this approach, Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds. The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes, at the expense of a small increase in detection time.
KW  - Fabric defect detection; multi-layer features; deformable convolution

DO  - 10.32604/iasc.2024.036897