Fabric Defect Detection System Based on MATLAB with Improved Otsu Method and Gabor Filtering

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With the advancement of automation technology, fabric defect detection has become an important issue in the textile industry. Traditional manual inspection is not only inefficient but also susceptible to subjective judgment, leading to the increasing attention on automatic detection methods based on computer vision.

This paper proposes a MATLAB-based fabric defect detection system that combines an improved Otsu’s method with Gabor filtering to achieve efficient and accurate detection. First, Gabor filters are used to extract texture features from fabric images. By applying filters in multiple directions and scales, the system enhances sensitivity to various texture patterns, thereby improving the ability to recognize surface details.

Next, the improved Otsu’s method is used for image binarization, automatically calculating the optimal threshold to accurately distinguish defect regions from normal areas. Finally, morphological operations are applied as post-processing to remove noise and refine the detection results.

Experimental results show that the proposed method achieves high detection accuracy across different types of fabric images. In particular, it demonstrates strong robustness and a low false detection rate when handling images with complex backgrounds and textures.