Abstract
Fabric defect detection is the important step of ensuring the quality and price of textiles. In order to make the automatic fabric defect detection system used in production sites, a cloud–edge collaborative fabric defect system is proposed. Firstly, real-time defect detection is performed on edge device, the accuracy of small defect detection is ensured by improved MobileNetV2-SSDLite. The channel attention mechanism is introduced in the network to highlight defect features and suppress background noise features. The loss function is redefined by Focal Loss to overcome the imbalance of the number of defects and background candidate boxes. Then, detection result storage and model update are carried out in the cloud. Experiments show that the accuracy of the system is improved while maintaining the faster detection speed, among which, the accuracy of the Camouflage dataset with small defects has increased by 10.03% and the detection speed reaches 14.19FPS on NVIDIA Jeston Nano.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.