Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large
Food image recognition plays a crucial role in food safety monitoring, nutritional analysis, and dietary recommendation systems. However, the diversity, complexity, and external factors such as lighting conditions pose numerous difficulties and challenges to the recognition task. In order to address...
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| Format: | Article |
| Language: | English |
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Academy of National Food and Strategic Reserves Administration
2025-03-01
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| Series: | Liang you shipin ke-ji |
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| Online Access: | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250214?st=article_issue |
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| author | HE Wei-chan YANG Zhi-jing QIN Jing-hui |
| author_facet | HE Wei-chan YANG Zhi-jing QIN Jing-hui |
| author_sort | HE Wei-chan |
| collection | DOAJ |
| description | Food image recognition plays a crucial role in food safety monitoring, nutritional analysis, and dietary recommendation systems. However, the diversity, complexity, and external factors such as lighting conditions pose numerous difficulties and challenges to the recognition task. In order to address these issues, this paper proposed a food image classification algorithm based on improved MobileNetV3-Large. Firstly, building upon the pre-trained MobileNetV3-Large model, the PReLu activation function and NAM attention mechanism were introduced to enhance the model's focus on key features by capturing non-local dependencies in images. Subsequently, a multi-task loss function was incorporated to further improve the classification performance by simultaneously optimizing multiple related tasks. Finally, the TrivialAugment data augmentation technique was employed to enhance the model's generalization ability by expanding the scale and diversity of the training dataset. Experimental results demonstrated that through these improvements, the model's accuracy on the Food-101 dataset increased from 66.9% to 84.2%, demonstrating the effectiveness of the proposed approach. |
| format | Article |
| id | doaj-art-54dbd9b4c7e44ad9ab5e948ead6fe2c7 |
| institution | Kabale University |
| issn | 1007-7561 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Academy of National Food and Strategic Reserves Administration |
| record_format | Article |
| series | Liang you shipin ke-ji |
| spelling | doaj-art-54dbd9b4c7e44ad9ab5e948ead6fe2c72025-08-20T03:44:33ZengAcademy of National Food and Strategic Reserves AdministrationLiang you shipin ke-ji1007-75612025-03-01332909610.16210/j.cnki.1007-7561.2025.02.013Research on Food Image Classification Algorithm based on Improved MobileNetV3-LargeHE Wei-chan0YANG Zhi-jing1QIN Jing-hui2School of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510000, ChinaSchool of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510000, ChinaSchool of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510000, ChinaFood image recognition plays a crucial role in food safety monitoring, nutritional analysis, and dietary recommendation systems. However, the diversity, complexity, and external factors such as lighting conditions pose numerous difficulties and challenges to the recognition task. In order to address these issues, this paper proposed a food image classification algorithm based on improved MobileNetV3-Large. Firstly, building upon the pre-trained MobileNetV3-Large model, the PReLu activation function and NAM attention mechanism were introduced to enhance the model's focus on key features by capturing non-local dependencies in images. Subsequently, a multi-task loss function was incorporated to further improve the classification performance by simultaneously optimizing multiple related tasks. Finally, the TrivialAugment data augmentation technique was employed to enhance the model's generalization ability by expanding the scale and diversity of the training dataset. Experimental results demonstrated that through these improvements, the model's accuracy on the Food-101 dataset increased from 66.9% to 84.2%, demonstrating the effectiveness of the proposed approach.http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250214?st=article_issuemobilenetv3-largenam attention mechanismprelu activation functiontrivialaugment data augmentation |
| spellingShingle | HE Wei-chan YANG Zhi-jing QIN Jing-hui Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large Liang you shipin ke-ji mobilenetv3-large nam attention mechanism prelu activation function trivialaugment data augmentation |
| title | Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large |
| title_full | Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large |
| title_fullStr | Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large |
| title_full_unstemmed | Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large |
| title_short | Research on Food Image Classification Algorithm based on Improved MobileNetV3-Large |
| title_sort | research on food image classification algorithm based on improved mobilenetv3 large |
| topic | mobilenetv3-large nam attention mechanism prelu activation function trivialaugment data augmentation |
| url | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250214?st=article_issue |
| work_keys_str_mv | AT heweichan researchonfoodimageclassificationalgorithmbasedonimprovedmobilenetv3large AT yangzhijing researchonfoodimageclassificationalgorithmbasedonimprovedmobilenetv3large AT qinjinghui researchonfoodimageclassificationalgorithmbasedonimprovedmobilenetv3large |