浮选泡沫图像特征提取方法研究进展
Research Progress of Flotation Froth Image Feature Extraction Method
宛鹤 1陆笑科 1屈娟萍 2薛季玮 1张崇辉 1王森 1卜显忠1
作者信息
- 1. 西安建筑科技大学资源工程学院,陕西 西安 710055
- 2. 奥卢大学奥卢矿业学院,奥卢 FI-90014,芬兰
- 折叠
摘要
机器视觉作为设备操作人员的工具,在泡沫浮选设备的监测中得到了广泛的应用.利用泡沫图像数据集建立预测识别模型,以初级泡沫特征参数为输入,以品位和回收率等浮选指标为输出.根据是否需要手动提取浮选泡沫图像特征,可以将特征提取算法划分为两大类别:一种是基于颜色、形态特征等的传统手动特征提取方法,另一种是基于深度神经网络的自动特征提取方法.本文总结并归纳了近年来浮选泡沫图像特征提取算法领域的研究进展,分析了各种方法的优势和不足,对当前难以人工识别泡沫状态及实现浮选自动化提升浮选效率,具有一定的指导价值.
Abstract
As a tool for equipment operators,machine vision has been widely used in the monitoring of froth flota-tion equipment.A predictive identification model has been developed,utilizing a froth image dataset,with primary froth characteristic parameters as inputs and flotation indicators like grade and recovery as outputs.Depending on the necessity for manual extraction of flotation froth image features,the feature extraction algorithms can be divided into two main categories:one is traditional manual feature extraction methods that rely on aspects such as color and morphological features,and the other is automatic feature extraction methods grounded in deep neural net-works.This paper summarizes the research progress in the area of flotation froth image feature extraction algorithms over recent years,while also critically examining the advantages and drawbacks of various methods it has certain guiding value for the curront difficulty in manually indentifying foam status and realizing flotation automation to im-prove flotation efficiency.
关键词
泡沫浮选/泡沫图像/机器视觉/泡沫图像特征Key words
froth flotation/froth image/machine vision/froth image features引用本文复制引用
基金项目
国家自然科学基金(52274271)
国家自然科学基金(52074206)
国家自然科学基金(52104266)
出版年
2024
国家科技期刊平台
NSTL
万方数据