机电工程技术2024，Vol.53Issue(4)：121-124,151,5.DOI:10.3969/j.issn.1009-9492.2024.04.027
基于YOLOv4改进算法的智能化冰柜商品数量检测方法
Intelligent Freezer Merchandise Quantity Detection Method Based on YOLOv4 Improved Algorithm
张斐 1文灏淳 1林浩彬 1吴鹏 1陈宝宇 1项振坡1
作者信息
- 1. 东莞理工学院机械工程学院,广东东莞 523808
- 折叠
摘要
Abstract
With the demand for intelligent freezers in unmanned supermarkets is gradually increasing,and the merchandise recognition technology of intelligent freezers is developing rapidly,such as radio frequency recognition,barcode recognition,two-dimensional code recognition and image recognition.However,due to signal interference,barcode obscuration,2D code folds and oversized recognition network model,the above recognition technologies have the problems of low recognition rate and slow speed.To address the above problems,a laser scattering distance measurement system is built and a method for detecting the number of goods in smart freezers is proposed based on the YOLOv4 improved algorithm.In the method,the convolutional layer and CSP module in the Backbone stage CSPDarknet53 are optimized,the 8-fold downsampling in the Neck stage us eliminated,and the problem of large model size and slow recognition speed of YOLOv4 algorithm are solved.The IR image under the laser scattering is obtained by using the IR camera,the depth map is obtained,and then the depth information is obtained with the color image,and the remaining quantity of goods is finally calculated.The improved algorithm is compared with YOLOv4 and YOLOv3 algorithms,and the results show that the improved YOLOv4 model mAP can reach 97.02%,and the model size is only 13.8 MB,which is 50%higher than the original YOLOv4 algorithm.This method can provide an efficient technical means for the remote management of commodity inventory.关键词
YOLOv4/激光散斑/图像匹配Key words
YOLOv4/laser scattering/image matching分类
信息技术与安全科学引用本文复制引用
张斐,文灏淳,林浩彬,吴鹏,陈宝宇,项振坡..基于YOLOv4改进算法的智能化冰柜商品数量检测方法[J].机电工程技术,2024,53(4):121-124,151,5.
