有色金属科学与工程2025，Vol.16Issue(1)：115-124,10.DOI:10.13264/j.cnki.ysjskx.2025.01.013
基于改进YOLOv5算法的选矿摇床矿带分离点目标检测识别研究
Recognition on ore zone separation points target detection and identification in mineral processing shaking table based on improved YOLOv5 algorithm
摘要
Abstract
The operation of shaking tables in mineral processing is influenced by multiple parameters,including feed rate,feed concentration,feed grade,and feed particle size,which cause variations in the position,color,and width of the ore bands on the table surface.To ensure the quality of the concentrate,it's necessary for workers to timely adjust the position where the concentrate is collected,maintaining the stability of the concentrate grade.Varying experience and skills of operators often lead to the fluctuations in production indicators.To reduce the labor intensity of operators and enhance the level of automation in mineral sorting with shaking tables,this paper introduces an improved YOLOv5 target detection algorithm,which successfully extracts the boundary points(ore band separation points)and marker information of both the concentrate band and the middling band on the shaking table.Compared with other algorithms such as YOLOv5,SSD,and Faster-RCNN,the improved YOLOv5 algorithm demonstrates the best detection performance with the highest precision,achieving an average precision of 98.3%.关键词
选矿摇床/YOLOv5/目标检测/自适应截取Key words
mineral processing shaking table/YOLOv5/target detection/adaptive interception分类
矿业与冶金引用本文复制引用
刘惠中,芮作为,朱合钧,彭志龙..基于改进YOLOv5算法的选矿摇床矿带分离点目标检测识别研究[J].有色金属科学与工程,2025,16(1):115-124,10.基金项目
江西省重点研发计划资助项目(20212BBE53026) （20212BBE53026）
