Development of a real-time home textile fabric defect inspection machine system for the textile industry

In most fabric industries fabric quality is assessed through manual inspection, which depends on an individual judgment. It is necessary to design an automatic fabric defect performance inspection system for the industry. This study aimed to develop a real-time, low-cost, and high-performance home textile fabric defect inspection machine system. The proposed system uses the Haar wavelet transform to reduce the information content of the fabric image. The brightness of the fabric image is compensated and the camera luminance is corrected in order to filter the image texture for fabric images with the Gaussian filter after correction. After that, the fabric defect classification was performed by using the random forest classifier. The designed system capability can detect and verify 10 kinds of fabrics with different colors. Moreover, the hardware cost of the machine is low and the average true defect recognition detection rate is more than 98.70%, with good adaptability. Meanwhile, the average processing detection time for a single image is 70 ms with a fabric defect inspection speed of 30 m/min. The efficiency of the machine is increased by five times compared with the traditional inspection. The designed inspection machine can also replace manual grading, cutting, and finishing in the processes of labeling defects. Eventually, it can reduced man power and overall mass production cost, so even small-scale home textile industries can afford a machine with high-precision defect detection.