Sichuan Cuisine Recognition Method based on Residual Neural Network
DOI:
https://doi.org/10.54097/fcis.v5i2.13148Keywords:
Residual Neural Network, Attention Mechanism, Image Classification, Sichuan Cuisine RecognitionAbstract
To address issues such as the high number of parameters, significant variations among images of similar dishes, weak geometric invariance, and low recognition rates in Sichuan cuisine recognition methods, a lightweight Sichuan cuisine recognition model, RGBNet, based on residual neural network, is proposed. The model employs dilated convolutions to increase the receptive field of convolutional kernels while maintaining a consistent parameter count, thus obtaining more shallow-level features. An RGB module is constructed using asymmetric convolutions to enhance the model's geometric invariance, feature non-linear expression, and feature extraction capabilities. Finally, the DFC long-range attention mechanism is introduced to effectively capture long-range information, thereby improving adaptive learning capabilities. To validate the model's performance, the classic ChineseFoodNet benchmark dataset is utilized. A MiniChineseFood dataset is created by extracting 30 classes totaling 20,000 images for experimentation. The recognition accuracy is measured using the top1 method of image recognition performance, achieving a final image recognition accuracy of 96.62%. Compared to models such as EfficientNet, ShuffNet, FasterNet, and MobileNetV2, RGBNet demonstrates respective accuracy improvements of 16.57%, 18.52%, 17.12%, and 16.35%. This presents a novel approach for industrial food recognition.
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