摘要
基于光学图像的煤矸石识别方法具有设备简单、易实现、绿色环保等优势,是实现智能化煤矸石分选的重要途径。该类方法分为两种研究路径,一种是需要人为提取特征进行识别的路径,一般包括煤矸图像数据采集、图像预处理、特征选择与提取和煤矸识别|另一种是利用深度学习神经网络进行自主提取特征识别的路径。文章对这两种研究路径的各类方法进行了总结,指出现有识别方法存在煤矸图像数据集不完备不充分、特征理解不全面不深入、识别方法无法兼顾高效与实时性等缺点,给出进行高效煤矸石识别的建议。
The coal gangue identification method based on optical image is important f`or intelligent coal gangue separation, which uses simple equipment, is easy to realize and environment-friendly. There are two research ways in this method, one way requires extracting artificial features for recognition, which generally includes four steps: coal and gangue image data set collection, image preprocessing, feature extraction and selection, and coal and gangue recognition. The other way uses deep learning neural network to independently extract features. The different methods in the two research ways are summarized, and it is pointed out that, the existing identification methods have shortcomings such as incomplete coal gangue image data set, incomplete feature understanding, and failure to give consideration to both efficiency and real-time performance. Suggestions for efficient coal gangue identification are put forward.
作者
张红
李晨阳
ZHANG Hong;LI Chen-yang(College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)
出处
《煤炭工程》
北大核心
2022年第7期159-163,共5页
Coal Engineering
关键词
煤矸石识别
图像识别
特征识别
机器学习
深度学习
coal gangue identification
image recognition
feature recognition
machine learning
deep learning
