分割
稳健性(进化)
计算机科学
人工智能
卷积神经网络
模式识别(心理学)
图像分割
过程(计算)
人工神经网络
深度学习
数据挖掘
生物化学
基因
操作系统
化学
作者
Yanqing Huang,Junfeng Jing,Zhen Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:70: 1-15
被引量:75
标识
DOI:10.1109/tim.2020.3047190
摘要
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.
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