起重运输机械Issue(9)：20-28,9.
基于机器视觉的紧密排列目标检测研究
摘要
Abstract
Machine vision technology is an important support for industrial robot vision guidance system.With the continuous rise of production efficiency,Many irregular and closely arranged goods are often transported together on the assembly line.Under such circumstances,the traditional target detection algorithm based on template matching has poor positioning performance and recognition accuracy.To solve this problem,YOLOv8 algorithm based on deep learning was used to detect closely arranged objects,and related data sets were established based on the characteristics of these objects,and the performance in structured environment was verified by experiments.The experimental results show that the model trained by YOLOv8 algorithm has a fast convergence speed and the average accuracy is stable at about 0.95,and in many real environments,the average accuracy of the model for identifying disorderly queue targets can reach 91.64%,which provides an effective solution for positioning and identifying closely arranged items on the assembly line.关键词
机器视觉/深度学习/目标检测/YOLOv8Key words
machine vision/deep learning/target detection/YOLOv8分类
信息技术与安全科学引用本文复制引用
许习军,张明远,孟文俊,武成柱,张涛..基于机器视觉的紧密排列目标检测研究[J].起重运输机械,2024,(9):20-28,9.基金项目
太原市双百攻关行动揭榜挂帅项目 （）
