Enhanced ResNet network for food image security recognition

Chen Chen; Dongmei Luo

doi:10.1117/12.2689785

31 July 2023 Enhanced ResNet network for food image security recognition

Chen Chen, Dongmei Luo

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 1274723 (2023) https://doi.org/10.1117/12.2689785
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

Food image security identification is a research hotspot in the fields of computer vision, data mining, food science and technology, etc. In order to solve the problem that the recognition rate of traditional food image security identification methods is not high because of the small difference between classes and large difference within classes, we propose an enhanced ResNet network food image security identification method. This method combines asymmetric convolution to enhance the learning of local skeleton information, and embeds the attention module shared by deep and shallow layers to solve the undifferentiated feature extraction of the whole image information, which improves the efficiency of feature extraction from local to global. Through a large number of experiments on food image data sets, the results show that the proposed algorithm makes the recognition accuracy of food images reach 85.26% and 96.21%, and it is 100% higher than the popular RESNET 101, RESNET-18 and RESNET-34 model methods. It further shows that the food image security recognition method in this paper will have a good application in small and medium-sized food image recognition systems.

Citation Download Citation

Chen Chen and Dongmei Luo "Enhanced ResNet network for food image security recognition", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 1274723 (31 July 2023); https://doi.org/10.1117/12.2689785

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KEYWORDS

Image enhancement

Network security

Feature extraction

Convolution

Machine learning

Computer security

Data modeling

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