Fault detection of fabrics using image processing methods

Yildiz, Kazım; Demir, Önder; Ülkü, Eyüp Emre

doi:10.5505/pajes.2017.09821

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Bo et al [15] propose a defect model based on Gabor filter, but the detection results for some types of defects are poor. Yildiz et al [16] Classified defect images by converting RGB images to binary images to improve detection performance. Although computer vision algorithms for automatic surface defect identification have demonstrated good results in detecting specific fabric defects, such approaches still need to be improved for slightly more complicated defects such as cycles, pattern types, etc.…”

Section: Defect Detectionmentioning

confidence: 99%

Effective fabric defect detection using contrastive learning and layered fusion network

Xie

Weijie

2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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Defect detection is a very important topic in industrial inspection. Due to the rapid expansion of computer computing power, manual-based defect detection approaches is no longer satisfactory, and deep learning-based detection techniques are increasingly being applied to defect detection tasks. contrastive learning is better for defect detection tasks due to the advantages of its label-free training method. So the Efficient Fabric Defect Detector (EFDD), a high-efficiency defect detector based on contrastive learning approach and layered fusion network, is proposed in our paper. First, we propose the Layered Fusion network to encode defect types with different scale sizes and improve the feature extraction ability of the model. Second, a universal structure with an attention module is introduced, this module can be integrated into ResNet with little overhead. Third, we investigate the impact of parallel enhancement strategies on model training in the data imbalance, different combinations can affect the model to different degrees. Finally, we evaluate the model using three classical public datasets and the average improvement of EFDD over the three datasets is AP 2.1%, AR 1.5% compared to other contrastive learning methods. Compared to the supervised methods, our model accuracy is close to or even exceeds that of the supervised methods.

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Section: Defect Detectionmentioning

confidence: 99%

Effective fabric defect detection using contrastive learning and layered fusion network

Xie

Weijie

2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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“…Bo et al proposed the machine vision technique in which defects are detected by the Gabor filter, which is based on image processing, however, it has poor detection results for some types of defects [9]. Wiener filter is used to classify defective images by converting RGB images into binary images to improve the detection effect [10]. In addition, there are other methods to detect fabric defects.…”

Section: Related Workmentioning

confidence: 99%

A Fabric Defect Detection Method Based on Deep Learning

Liu

Wang

et al. 2022

IEEE Access

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Fabric defect detection is a challenging task in the fabric industry because of the complex shapes and large variety of fabric defects. Many methods have been proposed to solve this problem, but their detection speed and accuracy were very low. As a classic deep learning method and end-to-end target detection algorithm, YOLOv4 has evolved rapidly and has been applied in many industries, showing good performance. This paper proposes an improved YOLOv4 algorithm with higher accuracy for fabric defect detection, in which a new SPP structure that uses SoftPool instead of MaxPool is adopted. The improved YOLOv4 algorithm with three SoftPools can process the feature map effectively, which has a significant advantage in reducing the negative side effects of the SPP structure and improving the detection accuracy. The improved SPP structure is used by the three outputs of Backbone, and in order to ensure that the output can be inputted into the subsequent PANet successfully, the network structure is improved that a series of convolution layers after the SPP structure is added for reducing the channel numbers of feature map to an appropriate value. In addition, contrast-limited adaptive histogram equalization is adopted in advance to improve the image quality, which results in strong anti-interference abilities and can slightly increase the mAP. Experimental results show that, compared with the original YOLOV4, the improved YOLOv4 increases the mAP effectively by 6%, while the FPS only decreases by 2. The improved YOLOv4 can identify the location of defects accurately and quickly, and can also be applied in other defect detection industries.

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“…Hand-crafted feature extraction is an efficient method for image processing, recognition, and computer vision. However, data size and image resolution advancements lead to extracting hand-crafted features, including morphology, area, shape, and more [18], [19]. Moreover, they are not robust, method dependent, and are computationally intensive due to high dimensionality.…”

Section: Introductionmentioning

confidence: 99%