CNN-Based Fabric Defect Detection System on Loom Fabric Inspection

Abstract

Fabric defect detection is generally performed based on human visual inspection. This method is not effective and it has various difficulties such as eye delusion and labor cost. To deal with these problems, machine learning, and computer vision-based intelligent systems have been developed. In this paper, a novel real-time fabric defect detection system is proposed. The proposed industrial vision system has been operated in real-time on a loom. Firstly, two fabric databases are constructed by using real fabric images and defective patch capture (DPC) algorithm. Thanks to the novel developed fast Fourier transform-based DPC algorithm, defective texture areas become visible and defect-free areas are suppressed, even on complex denim fabric textures. Secondly, an appropriate convolution neural networks (CNN) model integrated negative mining is determined. However, traditional feature extraction and classification approaches are also used to compare classification performances of deep models and traditional models. Experimental results show that our proposed CNN model integrated negative mining can classify the defected images with high accuracy. Also, the proposed CNN model has been tested in real-time on a loom, and it achieves 100% detection accuracy.

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“…In the realm of computer vision [1]- [3], the imperative task of object detection has undergone a paradigmatic evolution [4]- [6], catalysed by the revolutionary advent of the You Only Look Once (YOLO) architecture in 2016 [7]. YOLOs innovative approach diverged from the traditional two-stage object detection architectures, proposing a unified architecture with the ability to predict bounding boxes and class probabilities concurrently, all within the exigencies of real-time processing [8].…”

A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision

“…This warrants a stringent quality-inspection process, whereas the present manual inspection procedure has many drawbacks such as human fatigue, bias, downtime, labor costs, and low accuracy. Considering the success of deep learning, in particular, computer vision, in other domains [13][14][15][16][17][18], researchers are actively looking at the implementation of convolutional neural networks (CNN) for PV fault detection within a manufacturing setting. By doing so, the drawbacks associated with human inspection can be eliminated, for example, labor costs, fatigue, and bias.…”