Unsupervised UNet for Fabric Defect Detection

Kuan-Hsien Liu, Song-Jie Chen, Tsung-Jung Liu

20222022 IEEE International Conference on Consumer Electronics - Taiwan16 citationsDOI

Abstract

Currently, neural network based defect detection systems usually need to collect a large number of defect samples for training, and it takes a lot of manpower to mark labels and clean the subsequent data. This is a time-consuming process, and it makes the whole system less effective. In this paper, a neural network based method for fabric surface defect detection is proposed. By training positive clean samples, it can learn through neural network without collecting negative defective samples, which greatly shortens the landing time of whole system. Our proposed system can achieve 99% detection accuracy.

Topics & Concepts

Computer scienceArtificial neural networkArtificial intelligenceProcess (computing)Pattern recognition (psychology)Training setMachine learningComputer visionOperating systemIndustrial Vision Systems and Defect DetectionSurface Roughness and Optical MeasurementsImage and Object Detection Techniques