In this paper, the performance of Convolutional Neural Networks such as GoogleNet (Inception) and AlexNet are analyzed for the textile defect detection problem. The fabric images from ‘Cotton Incorporated’ database is used for this research work. The database images are converted to grey scale. The noises are removed from the grey scale image using Wiener filter. The noise free images are trained using GoogleNet and AlexNet to recognize new faults in the fabric. The identification of fabric fault by using GoogLeNet include image load, loading GoogLeNet network, loading pretrain network, freezing of the basic layers, and image validation. The steps in AlexNet for finding the fabric defects are image load, AlexNet network load, substitution of the final layers, training network, and image classification. According to the results of the experiment, GoogLeNet training on fabric defects is faster than that of AlexNet. The performance of GoogLeNet is the best outdoing than AlexNet on various parameter including time, accuracy, dropout, and the initial learning.
CITATION STYLE
Sudha, K. K., & Sujatha, P. (2019). A qualitative analysis of googlenet and alexnet for fabric defect detection. International Journal of Recent Technology and Engineering, 8(1), 86–92.
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