Steel Defect Classification Using Machine Learning

Arshad, Syeda Rabia; Obaid, Ishwa; Gull, Rameesha; Shahzad, Muhammad

doi:10.1109/imcom53663.2022.9721728

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Normalising pixel values is particularly advised for imaging modalities that don't directly correspond to absolute physical quantities. After that, we have used a pre-trained model by PyTorch [17] which had 135,310,918 total parameters out of which 1,050,374 are trainable while 134,260,544 are non-trainable parameters. The batch size was set to 128, Learning rate to 0.005, dropout rate to 0.4 [18] and the output size is 3.…”

Section: Vgg-16mentioning

confidence: 99%

Deep Learning Based Fabric Defect Detection

Arshad,

Shahzad

2024

Res. Rep. Comput. Sci.

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Ensuring quality standards is a crucial stage within the textile sector. Automated classification of the fabric defects is a vital step during the fabric manufacturing process in order to prevent any faulted fabric from being supplied to the market. The defects on the surface of the fabric were manually identified by the individuals but this poses problems in terms of human-error and is also time-consuming. Efforts have been made to achieve better precision in defect detection through image processing studies, leading to the development of automated systems. In this study, some high-performing deep learning models are applied including ResNet and VGG-16 and illustrated how these algorithms can be used in the domain of textile manufacturing for fabric defect detection. A combination of images are used ranging from patterned and textured to plain for better defects recognition on any given fabric. The algorithm VGG-16 has displayed 73.91% accuracy while the ResNet algorithm has shown 67.59% accuracy.

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Section: Vgg-16mentioning

confidence: 99%