An Image Recognition Method for the Foods of Northern Shaanxi Based on an Improved ResNet Network

Yonggang Ma (), Junmei Liu and Angang Cui
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Yonggang Ma: School of Mathematics and Statistics, Yulin University, Yulin 719000, China
Junmei Liu: School of Mathematics and Statistics, Yulin University, Yulin 719000, China
Angang Cui: School of Mathematics and Statistics, Yulin University, Yulin 719000, China

Mathematics, 2025, vol. 13, issue 16, 1-15

Abstract: With the development of artificial intelligence technology, food image recognition has become an important research direction in the field of computer vision. The region of Northern Shaanxi is famous for its rich food culture. This paper aims to propose a food image recognition method based on an improved ResNet network to enhance the recognition rate of characteristic foods in Northern Shaanxi. Firstly, the principles and structure of basic convolutional neural networks (CNNs) were introduced, with a focus on the application and optimization design of CNNs in food image recognition. This mainly included AC blocks fused with asymmetric convolutions, attention modules based on improving food image recognition performance, and residual structure design for enhancing learning effectiveness. Secondly, the FoodResNet18 model was constructed with a specially designed enhancement block and a deep, shallow shared attention residual module to enhance the feature extraction ability and perception of visual information by the model. To improve the generalization ability of the model, this paper comprehensively preprocessed the self-built Northern Shaanxi Food-300 dataset, covering the sources of data, processing methods, and data augmentation strategies used for training. The model training and comparative analysis show that the food image recognition method based on improved ResNet outperforms traditional CNN models in multiple experiments. In the ablation experiment, the specific contribution of the design module to the final recognition performance was analyzed, and the advantages of the deep shallow shared attention residual module in feature extraction and preservation were verified.

Keywords: food image recognition; ResNet network; CNN; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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