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| Design of Roll Paper Packaging Detection Model Based on Machine Vision |
| Received:August 23, 2022 |
| DOI：10.11981/j.issn.1000-6842.2023.04.85 |
| Key Words:roll paper packaging;machine vision;YOLOv4;deep learning |
| Fund Project:五邑大学港澳联合研发基金（2019WGALH21）；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 | | HUNG Qifeng* | Faculty of Intelligent Mamufacturing， Wuyi University， Jiangmen， Guangdong Province， 529000 | 529000 | | ZENG Zhiqiang | Faculty of Intelligent Mamufacturing， Wuyi University， Jiangmen， Guangdong Province， 529000 | 529000 | | HONG Zhiyong | Faculty of Intelligent Mamufacturing， Wuyi University， Jiangmen， Guangdong Province， 529000 | 529000 |
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| Abstract: |
| Aiming at the problems of low efficiency and high labor cost in roll paper packaging detection， a roll paper packaging detection model was designed based on machine vision and named F-YOLOv4. First， an industrial camera was used to collect target images in the process of roll paper packaging， and manually annotate them into a data set. Then， based on YOLOv4， a roll paper packaging detection model was built， and a lightweight mixed-channel attention module was introduced to enhance important features while avoiding the introduction of background noise. And the residual upsampling module was designed to improve the effect of upsampling. Finally， in the detection head part， the features of different resolutions were fused to enrich the feature map information. The experimental results showed that the accuracy of F-YOLOv4 was 97.53%， which was 1.97% higher than the original model， the detection speed was 129 f/s， and the model size was 39.7 MB. F-YOLOv4 can effectively solve the problem of roll paper packaging， reduce labor costs for enterprises， and improve production efficiency. |
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