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- W4200186232 abstract "The defect detection is an important activity in quality analysis and control in the fabric industry. The presented work gives a comparative analysis of artificial neural network and deep learning architectures. The MobileNet and deep residual network (ResNet) are deployed to classify the defective and nondefective fabric images. The hand-crafted morphological features are used in fabric image analysis along with feed backward selection feature reduction method to obtain the significant features. The overall classification rates of 95.3%, 98.2%, and 99.65% are obtained for Shallow, ResNet, and MobileNet architectures, respectively. The MobileNet model has given a maximum classification rate than Shallow and ResNet architectures. The work finds applications in apparel industry, quality analysis, cost estimation, online purchase of fabric, Industry 4.0, and so on." @default.
- W4200186232 created "2021-12-31" @default.
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- W4200186232 date "2021-12-02" @default.
- W4200186232 modified "2023-09-26" @default.
- W4200186232 title "Defective and nondefective classif ication of fabric images using shallow and deep networks" @default.
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- W4200186232 doi "https://doi.org/10.1002/int.22774" @default.
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