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| Real-time Defect Detection Algorithm for Roll Paper Packaging based on Improved YOLOv3 |
| Received:July 15, 2021 |
| DOI：10.11981/j.issn.1000-6842.2022.02.87 |
| Key Words:roll paper packaging;defect detection;convolutional neural networks;self-attention;multi-scale features |
| Fund Project:广东省基础与应用基础研究基金（2020A1515011468）；广东省普通高校特色创新类项目（2019KTSCX189）。 |
| Author Name | Affiliation | Postcode | | LI Zhicheng* | Faculty of Intelligent Manufacturing， Wuyi University， Jiangmen， Guangdong Province， 529000 | 529000 | | ZENG Zhiqiang | Faculty of Intelligent Manufacturing， Wuyi University， Jiangmen， Guangdong Province， 529000 | 529000 |
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| Abstract: |
| In order to solve the problem of defect detection in the process of roll paper packaging and improve the detection speed and accuracy of roll paper packaging defects， an improved YOLOv3 （iYOLOv3） algorithm was proposed. By combining convolutional neural network and multi-head self-attention， the iYOLOv3 algorithm could extract partial and global features of the image more adequately and it could further fuse the feature maps with different scales in multi-scale so that the decoding formular of YOLOv3 algorithm was improved： the AP@50∶5∶95 of iYOLOv3 algorithm was 5.8 percentage points higher than that of YOLOv3 algorithm， and its detection speed reached 80 frames/s， 2-folds more than that of the YOLOv3 algorithm， indicating its feasibility in practical application. |
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