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Automatic Detection and Classification of Weaving Fabric Defects Based on CV

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image processing technology to identify the sources of fabric defect in textile enterprises. Adopting image-recognition technology, through the fabric digital image pre-processing and recognition using feature detection and extraction of the fabric. Then the defect related information is stored in order to achieve automatic fabric defect detection which ultimately improves the efficiency of defect detection. Fabric defect detection system mainly includes three parts: The first, the fabric image acquisition, pre-processing part. The second, the identifier, for measurement and storage defect of fabric defect. The third, control mechanism automatically accomplished defect location and identifier. The proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state of-the-art, the limitations of several promising techniques are identified and performances are analysed in the context of their demonstrated results and intended application


Automatic fabric defect detection should be economical when reduction in labour cost and associated benefits are considered. The inspection system must also be robust and efficient. The advantage for the manufacturer here is to get a warning when a certain amount of defect or imperfection occurs during the production of the fabric so that precautionary measures can be taken before the product hits the market, because defects in fabrics can reduce the price of a product by 45% to 65%. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes which are characterized by their vagueness and ambiguity. Automated inspection of plain fabrics can detect 90% of the defects simply by thresholding. The aim of this research is to study an effective way to detect and classify defects


Existing System:

  • Pattern Recognition
  • LBP features Extraction


Block Diagram:

Fabric Defect Detection Based On Computer Vision


Proposed System:

The defect-free images are also computed as a reference images at the beginning of inspection for calibration. The mean and standard deviation from each of these images is used to locate defects. Then the decision rule is chosen to differentiate the features of inspecting image from the reference one. The information is then gathered from the different knowledge sources from same image to detail the defect pattern. It is referred as ‘data fusion’. This data fusion method is less noisy. The fused image is threshold afterword so as to eliminate the non-belonging pixel element. Defect segmentation is more accurate. This scheme also reduces false alarms. The defect segmentation is accurate for varying size, orientation, and resolution and therefore is robust, too



  • High complexity
  • Low performance in restoration of image quality



  • Accurate features extraction
  • Less algorithm complexity.
  • Its processing time is low.
  • Low complexity


Software Requirement:

  • Python Idle
  • Opencv
  • Numpy
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  • Instructor pantech team
  • Duration 15 Hrs
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  • Access 3 Months

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