Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks

Clicks: 288
ID: 114091
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #66 of 220 articles by views in energies

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 220 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Sorting gangue from raw coal is an essential concern in coal mining engineering. Prior to separation, the location and shape of the gangue should be extracted from the raw coal image. Several approaches regarding automatic detection of gangue have been proposed to date; however, none of them is satisfying. Therefore, this paper aims to conduct gangue segmentation using a U-shape fully convolutional neural network (U-Net). The proposed network is trained to segment gangue from raw coal images collected under complex environmental conditions. The probability map outputted by the network was used to obtain the location and shape information of gangue. The proposed solution was trained on a dataset consisting of 54 shortwave infrared (SWIR) raw coal images collected from Datong Coalfield. The performance of the network was tested with six never seen images, achieving an average area under the receiver operating characteristics (AUROC) value of 0.96. The resulting intersection over union (IoU) was on average equal to 0.86. The results show the potential of using deep learning methods to perform gangue segmentation under various conditions.
Reference Key
gao2020energiesautomatic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rong Gao;Zhaoyun Sun;Wei Li;Lili Pei;Yuanjiao Hu;Liyang Xiao;Gao, Rong;Sun, Zhaoyun;Li, Wei;Pei, Lili;Hu, Yuanjiao;Xiao, Liyang;
Journal energies
Year 2020
DOI
10.3390/en13040829
URL
https://doi.org/10.3390/en13040829
Keywords
Segmentation feature extraction u-net coal and gangue Segmentation feature extraction u-net coal and gangue

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.