计算机应用与软件2024，Vol.41Issue(11)：319-326,365,9.DOI:10.3969/j.issn.1000-386x.2024.11.044
改进YOLOv4-tiny网络的日用商品目标检测算法
AN IMPROVED DAILY COMMODITY TARGET DETECTION ALGORITHM BASED ON YOLOV4-TINY NETWORK
摘要
Abstract
For the problems of high hardware requirements,complex model and low accuracy of commodity image detection algorithm based on mobile platform,an improved network based on YOLOv4-tiny is proposed,which can reduce the network parameters and model size,improve the network accuracy and build a more efficient network.The original standard convolution was replaced by point convolution and depth convolution,and CG module was used for feature extraction to reduce the calculation loss of network model.In feature fusion,PANity module was added to the original feature pyramid(FPN)to shorten the span of convolution layer between high and low.The CSPConcat structure was used to optimize the fusion features of each layer,which improves the ability of feature fusion.k-prototypes algorithm was used to optimize the size and number of prior boxes in daily commodity data set.Through the experiment on the daily commodity data set under the framework of Darknet deep learning,it is concluded that the average accuracy(map)of the improved algorithm is 98%,the recall rate is 97%,which is 2.4 and 2 percentage points higher than the original network,the calculation amount of the network model is 40.4%lower than the original network,and the storage file of the model is 55.9%smaller.The experimental results show that the improved network model is lighter and more accurate,which is more suitable for low hardware level embedded devices deployed in unmanned settlement link.关键词
新零售/嵌入式/目标检测/日用商品/YOLOv4-tinyKey words
The new retail/Embedded/Target detection/Commodities for daily use/YOLOv4-tiny分类
信息技术与安全科学引用本文复制引用
王林枫,左云波,徐小力,周可鑫,范博森..改进YOLOv4-tiny网络的日用商品目标检测算法[J].计算机应用与软件,2024,41(11):319-326,365,9.基金项目
北京学者计划项目(2015-025) （2015-025）
促进高校内涵发展重点培育项目(5212010976). （5212010976）
