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| Paper Defect Detection Method Based on Improved YOLOv5 |
| Received:May 27, 2022 |
| DOI：10.11980/j.issn.0254-508X.2022.10.012 |
| Key Words:paper defect detection YOLOv5 batch normalization coordinate attention loss function |
| Fund Project:陕西省榆林市2020年科技计划项目（CXY-2020-090）。 |
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| Abstract:Aiming at the problems of difficulty in extracting paper defect features and poor real time in current paper defect detection algorithms， an improved YOLOv5 paper defect detection method was proposed in this study. The method was firstly adding centering and scaling calibration to the head and tail of the batch normalization module to form a more stable effective feature distribution of paper defects. Secondly， a coordinate attention was added to the backbone network to enhance the paper defect feature extraction capability of the backbone network. Finally， CIoU_loss was selected as the loss function of bounding box regression to achieve high-precision positioning. The experimental results showed that the average accuracy of the improved algorithm reached 99.02%， the real-time detection speed reached 41.58 frames/s. Compared with the existing CNN-based paper defect classification algorithm， the detection accuracy and detection speed were greatly improved. The improved algorithm was less dependent on the light source and could accurately identify various paper defects. |
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