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- Title
基于改进YOLOv7网络的织物疵点检测.
- Authors
石玉文; 林富生; 宋志峰; 余联庆
- Abstract
A iming at the failure of traditional target detection methods to balance detection accuracy prediction speed and lightweight deployment model to realize real-time defects detection a lightweight detection model based on improved YOLOv7 network was proposed* Firstly, lightweight convolution Ghost conv was introduced into the backbone network to reduce the number of network parameters while ensuring the detection accuracy and. improve the detection efficiency of fabric defects* Secondly CBAM attention mechanism was added to suppress useless information and enhance feature extraction ability* Finally, a new measurement method ot-SIoU was introduced to replace loU in the regression loss function to accelerate the degree of freedom of the loss function and improve the accuracy of the network modeL The experiment shows that the accuracy P of the detection model reaches 96. 27%, the average precision mAP value is 83. 84% > the model size is only 19. 10 MB, which effectively balances the accuracy realtime and lightweight deployment of defects detection.
- Subjects
DEGREES of freedom; FORECASTING
- Publication
Wool Textile Journal, 2024, Vol 52, Issue 8, p103
- ISSN
1003-1456
- Publication type
Article
- DOI
10.19333/j.mfkj.20231200308