文章基本信息

标题：An Improved YOLOv3 Model for Asian Food Image Recognition and Detection
本地全文：下载
作者：Xiaopei He ; Dianhua Wang ; Zhijian Qu 等
期刊名称：Open Journal of Applied Sciences
印刷版ISSN：2165-3917
电子版ISSN：2165-3925
出版年度：2021
卷号：11
期号：12
页码：1287-1306
DOI：10.4236/ojapps.2021.1112098
语种：English
出版社：Scientific Research Publishing
摘要：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.
关键词：Asian Food;YOLOv3;Feature Fusion;Complete-IOU;CBAM