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Textile industries play a major role in the growth of the economy of developing and developed countries. The faulty parts in the fabric are the main problem for the textile industry that majorly affects the quality of the fabric. The faulty parts are difficult to inspect and detect manually. Typically, the textile industry inspects faulty parts in the fabric through a human inspection system that is time-consuming and costly. More importantly, due to human inspection, fewer defects are inspected, which ultimately affects the quality of fabric. Due to this reason, the economic progress of the textile industry is directly affected. Therefore, it is time to develop automatic real-time fabric fault inspection and detection techniques equipped with a computer vision system. The automatic systems greatly improve the accuracy, reliability, and speed compared to the human inspection system. In addition, the automatic inspection and detection system provides a high fault detection rate. The automated system helps reduce labor costs, improves the quality of the product, and increases the efficiency of the manufacturing process. In the proposed work, the discrete wavelet is first realized to inspect the uniformity of fabric using digital fabric images. As the perfect fabric has a steady intervallic structure, the fault in the fabric disrupts the steady formation. Therefore, checking disruption in the fabric thresholding process is also realized. Consequently, the proposed system can detect and precisely locate the defect in the fabric under consideration.

An efficient scheme for the detection of defective parts in fabric images using image processing