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- W4287515008 abstract "The textile production is one of the significant income to the Indian economy. In textile industry, defective production of cloth leads to the loss of profit and quality. To improve the quality of production and reduce the production loss, it is essential to have a system, to monitor the cloth quality after fabrication. Manually detecting the defect is efficient, however it is time consuming and fine defects may not be detected. Many researchers have proposed defective detect systems based on machine learning techniques, with effective, fast and fine defect detection. Recently, Deep Learning techniques have emerged and it results are better than machine learning techniques. In this proposed work, a fabric defect detection system that uses deep learning techniques mainly Convolutional Neural Networks (CNN) has been developed. In this presented research, various optimizers are used, and the performance has been evaluated by using the fabric dataset called ZJU-Leaper. The proposed work provides an improved efficiency of 97.40% using ADAM optimizer." @default.
- W4287515008 created "2022-07-25" @default.
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- W4287515008 date "2022-01-01" @default.
- W4287515008 modified "2023-09-29" @default.
- W4287515008 title "Fabric Defect Detection Using Deep Learning Techniques" @default.
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- W4287515008 doi "https://doi.org/10.1007/978-981-19-2541-2_9" @default.
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