Dual-path segmentation network for automatic fabric defect detection

Abstract

Fabric defect detection plays a crucial role in the production process of the textile industry. Vision-based inspection methods have emerged as an inevitable trend due to their lower labor costs and high detection efficiency. As the accuracy requirements for fabric defect detection, methods must not only identify and locate defects accurately but also describe the morphological features of the defects. This poses a challenge for the algorithm’s design, as it must consider both the semantic and texture information of the fabric. In this paper, we propose an end-to-end dual-path segmentation network called DPNet for fabric defect detection, which can extract and fuse both semantic and texture information to achieve high accuracy. The proposed framework consists of two paths: the semantic path, which has narrow but deep layers to obtain high-dimensional features, and the texture path, which has wide and dense layers to extract low-level details. To enhance the interaction between semantic and texture features, a crossed attention fusion module has been developed. Evaluations show that the proposed method outperforms other methods on different datasets in terms of mIoU, with results of 75.84% for Ngan and 70.69% for AITEX. In addition, we developed an inspection platform and tested the proposed method online. We found that it can achieve online detection at a speed of 40 m/min, making it well-suited for practical production environments.