Author
Qi Hu
Bio: Qi Hu is an academic researcher from Wuhan University of Science and Technology. The author has contributed to research in topics: Machine vision & Sorting. The author has an hindex of 2, co-authored 3 publications receiving 17 citations.
Papers
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TL;DR: An exploratory study employing a bench-scale approach to detect the multi-information of coal quality online by machine vision simultaneously, including particle size distribution, density distribution, the ash content of each density fraction, and the total ash content.
35 citations
08 Oct 2020
TL;DR: A competitive voting method to improve the multi-class prediction accuracy of ores in machine vision-based sorting system by combining the classification advantages of various machine learning methods is proposed.
Abstract: Sensor-based intelligent sorting technology is a mineral separation technology with the merits of high-efficiency, energy-saving and water-saving. However, the prediction accuracy of conventional machine learning methods is unstable in multi-class selection of ores. The purpose of this study is to propose a competitive voting method to improve the multi-class prediction accuracy of ores in machine vision-based sorting system by combining the classification advantages of various machine learning methods. The operations of image segmentation, feature extraction and feature selection are presented to obtain the multi-class datasets. Three ones of traditional machine learning models with higher classification accuracies are used to establish competitive voting classification models. A case study using the image data of a gas coal shows the merits of the proposed approach. Results derived using this competitive voting approach reveal that it outperforms preexisting approaches.
6 citations
01 Oct 2019
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TL;DR: Zhang et al. as discussed by the authors proposed a deep learning-based image segmentation method to segment the key areas in mineral images using morphological transformation to process mineral image masks, including backbone selection, module configuration, loss function construction, and its application in mineral image classification.
78 citations
TL;DR: This paper attempts to explore a more suitable small deep learning model for ore image classification by considering the model depth, model structure, and dataset size.
60 citations
TL;DR: The EfficientNet was adopted as the backbone feature extraction network instead of Darknet53 in the improved Yolov3 algorithm, which comprehensively considers the balance between network depth, width, and image resolution for network scaling to improve the accuracy of the algorithm in limited computing resources.
38 citations
TL;DR: This study builds four CNNs models with different depth and structure for multi-coal and multi-class image classification based on VGG Net, Inception Net, and Res Net and proposes a universal CNNs model suitable forMulti- coal andMulti-class sorting.
24 citations
TL;DR: The results of the work have been obtained within the framework of the research-and-development work "Development of advanced technologies for the complete mining of steam coal with the accumulation of waste rocks in the underground space".
Abstract: The results of the work have been obtained within the framework of the research-and-development work “Development of advanced technologies for the complete mining of steam coal with the accumulation of waste rocks in the underground space” (No. 0120U101099). The authors are grateful to the specialists of PJSC “DTEK Pavlohradvuhillia” for the information on the mine provided for research-and-technology analysis.
19 citations