Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved U-net

Lei, Meng; Rao, Zhongyu; Wang, Hongdong; Chen, Yilin; Zou, Liang; Yu, Han

doi:10.1016/j.fuel.2021.120475

Cited by 28 publications

(13 citation statements)

References 33 publications

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“…The obtained results can be related to others reported in the literature [56,68]; however, the comparison is not obvious as the mentioned papers do not provide the measures for the macerals' groups separately. Therefore, it is reasonable to use the mean values for the IoU and MIoU presented in Table 3 and the values of the same measures presented in [68]. The presented U-Net-based network gives better results than the non-DL methods.…”

Section: Resultssupporting

confidence: 72%

“…The presented U-Net-based network gives better results than the non-DL methods. The results obtained by the improved U-Net (enhanced with the use of attention gates) are better than presented in the paper, though the difference is small (IoU ~0.8554 and MIoU ~0.631 for best enhanced network presented in [68]). However, it is impossible to assess how it is divided into individual macerals groups.…”

Section: Resultscontrasting

confidence: 58%

“…When the liptinite, with the worst results, is omitted, the mean IoU and MIoU for inertinite and vitrinite are much greater. As the proposed models address the segmentation of each of the macerals individually, they should be treated as complementary to the model presented in [68]. Wang et al presents the results obtained using different deep learning networks architectures, such as U-Net (standard multi-class architecture), SegNet, and DeepLab V3+ [57].…”

Section: Resultsmentioning

confidence: 99%

“…The authors reported impressive accuracy, exceeding 98%, in rock type recognition. The application of semantic segmentation to maceral group identification was developed by Lei et al [68]. The proposed network utilizes the U-Net [69] network enhanced with the attention gates.…”

Section: Introductionmentioning

confidence: 99%

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Application of Deep Learning in Petrographic Coal Images Segmentation

Iwaszenko¹,

Róg²

2021

Minerals

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The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work.

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Section: Resultssupporting

confidence: 72%

Section: Resultscontrasting

confidence: 58%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Deep Learning in Petrographic Coal Images Segmentation

Iwaszenko¹,

Róg²

2021

Minerals

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“…In recent years, driven by the development of big data and computational capability, deep learning models represented by convolutional neural network (CNN) has achieved remarkable success in image processing [21,22], natural language processing [23,24], and speech recognition [25,26]. Different from traditional methods, deep learning models are capable of automatically extracting high-level features from the high-dimensional input data through hierarchical structures [27].…”

Section: Introductionmentioning

confidence: 99%

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

Zou

Liu

Lei

et al. 2021

Entropy

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Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.

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