TITLE:
An Improved YOLOv3 Model for Asian Food Image Recognition and Detection
AUTHORS:
Xiaopei He, Dianhua Wang, Zhijian Qu
KEYWORDS:
Asian Food, YOLOv3, Feature Fusion, Complete-IOU, CBAM
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.11 No.12,
December
30,
2021
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.