The home textile defect detection system can detect warp break, weft break, coarse knot, warp break, weft file, loose edge, pilling, stain, hole and so on, automatically detection the defect location, defect classification, edge marking, quality assessment and other functions in one system. The system can be used for a variety of different textiles detection, the detection speed and accuracy had reach the international level of similar products. It has the advantages of easy to use and maintenance, cost-effective. Can effectively improve the efficiency of automatic detection of textile surface defects.
In the automatic or semi-automatic production processing, an essential link is the defect detection of finished products to improve the yield. In the past, mostly was manual detection , but this method was limited to the subjective factors of personnel, and easily affected by people fatigue, concentration and other factors, this will lead to a lower accuracy of detection results, and it was difficult to achieve uniformity in the standard.Therefore, visual defect detection system can be an effective way to achieve intelligent production and precise control, with high speed, high accuracy, stability and reliability and some other advantages.
For home textiles, Keye has developed a fabric defect detection system based on AI deep learning. Once an error is detected by an industrial camera, the system will issue instructions to stop the machine's action. Therefore, in the application of textile industry, the Visual defect detection technology effectively improves the pain spots of waste fabric for manufacturers.
In addition, the visual defect detection system can detects the defects of home textile fabric and cord fabric one by one, and the detection rate of various defects such as broken warp, broken weft, holes, stains and mosquitoes is greatly improved compared with manual ones, and the output of the results is stable and reliable.
Through the on-line inspection of the surface quality of the fabric, the defects can also be quantified and classified to provide the basis reasons for the causes of the defects and reduce the rate of defective products from the root.
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