%0 Conference Proceedings
%T Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny
%+ Amrita University
%A Sujee, R.
%A Shanthosh, D.
%A Sudharsun, L.
%Z Part 2: Computational Intelligence for Image and Video Analysis
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 3rd International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Chennai, India
%Y Aravindan Chandrabose
%Y Ulrich Furbach
%Y Ashish Ghosh
%Y Anand Kumar M.
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-578
%P 196-204
%8 2020-02-20
%D 2020
%R 10.1007/978-3-030-63467-4_15
%K YOLOv2
%K YOLOv3 Tiny
%K Fabric defect detection
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-03434800/document
%2 https://inria.hal.science/hal-03434800/file/507484_1_En_15_Chapter.pdf
%L hal-03434800
%U https://inria.hal.science/hal-03434800
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-578