Abstract
The detection and recognition of food pictures has become an emerging application field of computer vision. However, due to the small differences between the categories of food pictures and the large differences within the categories, there are problems such as missed inspections and false inspections in the detection and recognition process. Aiming at the existing problems, an improved YOLOv3 model of Asian food detection method is proposed. Firstly, increase the top-down fusion path to form a circular fusion, making full use of shallow and deep features. Secondly, introduce the convolution residual module to replace the ordinary convolution layer to increase the gradient correlation and non-linearity of the network. Thirdly, introduce the CBAM (Convolutional Block Attention Module) attention mechanism to improve the network’s ability to extract effective features. Finally, CIOU (Complete-IoU) loss is used to improve the convergence efficiency of the model. Experimental results show that the proposed improved model achieves better detection results on the Asian food UECFOOD100 data set.
Highlights
With the development of science and technology and the improvement of human living standards, food object detection plays an important role in the fields of digital retail services, smart homes, healthy eating, self-eating detection, etc
In 2015, Bettadapura [4] used the food pictures taken by the camera and other information such as the location of the restaurant involved in the pictures to classify the food through support vector machines (SVM)
At the beginning of training, due to the limited Graphics Processing Unit (GPU) memory, the initial learning rate is set to 1e−3, the gamma coefficient is set to 0.92, and the batch size is set to 8
Summary
With the development of science and technology and the improvement of human living standards, food object detection plays an important role in the fields of digital retail services, smart homes, healthy eating, self-eating detection, etc. In 2009, Taichi Joutou [3] proposed an automatic food image recognition system, which uses Multiple Kernel Learning (MKL) method to integrate multiple image features to classify food images. In 2019, Zhang Gang and others [7] proposed a food image recognition method based on diffusion graph convolutional network and transfer learning, Weiqing Min [8] and others used abundant raw material composition information to locate multiple food images of different scales, and realized the recognition from the category level to the composition level. As far as Asian food is concerned, its shape and structure are diverse, and the appearance of food under different cooking methods is very different These factors significantly increase the difficulty of detection. The CIOU (Complete-IoU) positioning loss function is used to fully consider the integrity of the food and improve the convergence efficiency of the model
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