Real-Time Fabric Defect Detection using Deep Learning

dc.contributor.author	WAQAR, ZAVEEN
dc.date.accessioned	2023-08-03T05:39:56Z
dc.date.available	2023-08-03T05:39:56Z
dc.date.issued	2021
dc.identifier.other	206728
dc.identifier.uri	http://10.250.8.41:8080/xmlui/handle/123456789/35459
dc.description	Supervisor: DR HASAN SAJID	en_US
dc.description.abstract	Defect detection in fabric production lines is a crucial and indispensable task to maintain quality in the textile industry. The current manual annotation scheme causes the fabric industry considerable losses. In order to address this issue a real-time detection system is proposed in this thesis. The system is based on a SOTA deep-learning detection algorithm which is optimized to achieve real-time performance. The detection architecture is trained on a self-gathered SOTA dataset from an industrial environment. Deploying the trained model on an actual real-time operating compactor resulted in 89% accuracy when evaluated	en_US
dc.language.iso	en	en_US
dc.publisher	School of Mechanical & Manufacturing Engineering (SMME),NUST	en_US
dc.relation.ispartofseries	SMME-TH-604;
dc.subject	Detection, Single-Shot, Faster-RCNN, Fabric Defects	en_US
dc.title	Real-Time Fabric Defect Detection using Deep Learning	en_US
dc.type	Thesis	en_US