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- Title
改进YOLOv5 的织物疵点检测算法.
- Authors
马阿辉; 祝双武; 李丑旦; 马晓彤; 王世豪
- Abstract
Aiming at the problems of slow detection speed of two- stage algorithm and low detection accuracy of onestage algorithm in the current network model applied to fabric defect detection, an improved YOLOv5 fabric defect detection algorithm is proposed. Firstly, for the different sizes of fabric defects, the clustering distance standard of the K -mean algorithm is modified, and the size of the priori frame is recalculated. Secondly, the standard convolution(SC)of the network Neck layer is improved, and the depth separation convolution(DSC)is combined with the standard convolution to reduce the amount of network layer parameters while maintaining the feature extraction capability of the network. The coordinate attention(CA)mechanism is introduced in the feature fusion stage, so that the network can capture the connection between each channel while retaining the precise positioning information of the target, thereby enhancing the feature extraction and positioning capabilities of the network. Finally, the weighted bidirectional feature pyramid network(BiFPN) is used, the feature pyramid module is modified to achieve simple and fast multi-scale feature fusion. After training on the data set, the results show that the mAP value of the improved YOLOv5 model can reach 97.4%, which is 2.8 percentage points higher than the original network accuracy, which meets the requirements of fabric defect detection.
- Subjects
PYRAMIDS; ALGORITHMS; TEXTILES; NECK; NANOPOSITIONING systems; FEATURE extraction
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 10, p244
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2209-0413