Fabric Defect Segmentation Method Based on Deep Learning

doi:10.1109/TIM.2020.3047190

Fabric Defect Segmentation Method Based on Deep Learning

2021-01-01

81

PRE

AI

DOI：10.1109/TIM.2020.3047190

Original

Original request for help

Save

Abstract

En 中文

Fabric defect detection plays an essential role in the textile production process, which was widely applied in the textile industry. For fabric defect detection, many algorithms have been proposed. However, lots of important problems, such as the accuracy of detection, the computational complexity of the algorithm, and data imbalance, still needed to be addressed for application in industrial production. In this article, we propose an efficient convolutional neural network for defect segmentation and detection. The design of this framework significantly alleviates the manual annotation cost of the data set; it only needs few defect samples combined with standard samples to learn the potential feature of defects and obtain the location of defects with high accuracy. The network is divided into two parts: segmentation and decision. First, the fabric data set without training is utilized as the input of the segmentation network. Then, the output of the segmentation network is applied as the raw materials for training the decision network. Finally, a well-trained network is used to obtain the location of defects with high accuracy. The proposed method only demands almost 50 defect samples to get accurate segmentation results and can achieve the requirement of real-time detection with a speed of 25 frames per second (FPS). The experimental results based on a public data set and three self-made fabric data sets show that the proposed method significantly outperforms eight state-of-the-art methods in terms of accuracy and robustness.

Keywords:

Deep learning

defect detection

fabric defect segmentation

few-shot learning

neural network

Fabric Defect Segmentation Method Based on Deep Learning

Abstract

AI Summary

Journal

Organization