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Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny

Abstract : The paper aims to classify the defects in a fabric material using deep learning and neural network methodologies. For this paper, 6 classes of defects are considered, namely, Rust, Grease, Hole, Slough, Oil Stain, and, Broken Filament. This paper has implemented both the YOLOv2 model and the YOLOv3 Tiny model separately using the same fabric data set which was collected for this research, which consists of six types of defects, and uses the convolutional weights which were pre-trained on Imagenet dataset. Observed and documented the success rate of both the model in detecting the defects in the fabric material.
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Conference papers
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https://hal.inria.fr/hal-03434800
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Submitted on : Thursday, November 18, 2021 - 2:21:25 PM
Last modification on : Thursday, November 18, 2021 - 2:31:54 PM
Long-term archiving on: : Saturday, February 19, 2022 - 7:13:51 PM

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R. Sujee, D. Shanthosh, L. Sudharsun. Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.196-204, ⟨10.1007/978-3-030-63467-4_15⟩. ⟨hal-03434800⟩

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