Defect Detection in Fabric using Wavelet Transform and Genetic Algorithm

Harinath, Depavath; Babu, K. Ramesh; Satyanarayana, P.; Murthy, M. V. Ramana

doi:10.14738/tmlai.36.1551

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…However, this method does not generalize well for some slightly complex patterned textures. Harinath et al [ 14 ] proposed a wavelet transform-based method for fabric defect detection, which is well suited for quality inspection due to its multi-resolution feature extraction. However, similar spectral approaches are usually computationally demanding.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

237

121

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Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

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Section: Introductionmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

237

121

View full text Add to dashboard Cite

show abstract

“…This method is not efficient in detecting low‐contrast fabrics and patterned fabrics. The spectral methods [8, 9], widely used in the study of fabric defects, highlight the differences between defective and defect‐free regions in the frequency domain, which can effectively realise the detection of fabrics with a simple background. Typical spectral methods include Fourier transform, wavelet transform [10], and Gabor transform [11].…”

Section: Introductionmentioning

confidence: 99%