Publication Type:

Conference Paper

Source:

Computational Intelligence in Data Science, Springer International Publishing, Cham (2020)

ISBN:

9783030634674

URL:

https://link.springer.com/chapter/10.1007/978-3-030-63467-4_15

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.

Cite this Research Publication

Sujee R., Shanthosh, D., and Sudharsun, L., “Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny”, in Computational Intelligence in Data Science, Cham, 2020.