基于MSR-UNet的煤岩显微组分分割与定量分析算法

doi:10.11871/jfdc.issn.2096-742X.2023.06.012

数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (6): 126-137.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.06.012

doi: 10.11871/jfdc.issn.2096-742X.2023.06.012

基于MSR-UNet的煤岩显微组分分割与定量分析算法

季菁菁¹(),奚峥皓^1,^*(),李忠峰²

1.上海工程技术大学，电子电气工程学院，上海 201620
2.营口理工学院，电气工程学院，辽宁营口 115000

收稿日期:2022-06-13 出版日期:2023-12-20 发布日期:2023-12-25
通讯作者: *奚峥皓（E-mail: zhenghaoxi@sues.edu.cn）
作者简介:季菁菁，上海工程技术大学，电子电气工程学院，硕士研究生，研究方向为深度学习图像处理。
本文主要承担工作为：模型设计、实验数据分析、文章撰写。
JI Jingjing is a graduate student studying in the school of electronic and electrical engineering at Shanghai University of engineering and technology. Her main research interests include deep learning for image processing.
In this paper, she is mainly responsible for model design, experimental data analysis and article writing.
E-mail: jingjing9668@163.com|奚峥皓，上海工程技术大学，副教授，研究方向为智能感知计算、人工智能及自然语言处理。
文本主要承担工作：优化指导。
XI Zhenghao, Shanghai University of Engineering and Technology, Ph.D., associate professor. His main research interests include intelligent perception computing, artificial intelligence, and natural language processing.
In this paper, he is mainly responsible for the optimization guidance.
E-mail: zhenghaoxi@sues.edu.cn
基金资助:
国家自然科学基金“面向广义宽基线立体像对的目标三维重建技术研究”(61801286)

The Study of Coal Macerals Segmentation and Quantitative Analysis Based on MSR-UNet

JI Jingjing¹(),XI Zhenghao^1,^*(),LI Zhongfeng²

1. School of Electronic and Electrical Engineering, Shanghai University of Engineering and Technology, Shanghai 201620, China
2. School of Electrical Engineering, Yingkou Institute of Technology, Yingkou, Liaoning 115000, China

Received:2022-06-13 Online:2023-12-20 Published:2023-12-25

摘要/Abstract

摘要：

【目的】 煤岩的质量决定着煤岩的利用率，其组分的分析是判断煤炭质量的重要依据。本文针对煤岩显微组分的分割问题，提出一种改进的UNet网络，旨在提高对煤岩显微组分分割的准确度，以此实现对煤岩显微组分的自动化分析。【方法】 首先，提出一种多尺度上下文注意模块。通过捕获具有空间上下文信息的高层特征来提高网络提取关键特征的能力。其次，在跳跃连接层中引入挤压激励模块，提高网络对低层特征重要信息的捕获能力。最后，选用骰子损失函数和焦点损失函数训练网络，以提高网络对小目标组分的敏感度和对相似组分的区分能力。【结果】 实验结果表明，所提方法在分割煤岩显微组分图像时，PA指标、IoU指标和Dice指标分别为91.24%、83.01%和84.70%，各组分分割的平均绝对误差分别为2.95%、5.43%和6.19%。【结论】 本文算法在实现利用计算机辅助自动化分析煤岩质量方面具有巨大潜力。

关键词: 煤岩显微组分, 图像分割, UNet, 多尺度上下文注意模块, 挤压激励模块

Abstract:

[Objective] The utilization rate of coal macerals is mainly determined by the quality of coal, and the analysis of its composition is an important basis for judging the quality of coal. This paper proposes an improved UNet for the segmentation of coal maceral micro-components. The purpose is to improve the accuracy of the segmentation of coal micro-components so as to realize the automatic analysis of coal maceral micro-components. [Methods] Firstly, a multi-scale contextual attention module is proposed. It can improve the ability of the network to extract key features by capturing high-level features with spatial contextual information. Secondly, a squeeze and excitation module is introduced in the jump connection layer to improve the network's ability to capture important information about low-level features. Finally, the dice loss function and the focal loss function are selected to train the network to improve the sensitivity of the network to small target components and the ability to distinguish similar components. [Results] The experimental results show that the proposed method performs well in segmenting the microscopic component images of coal rocks with the PA, IoU, and Dice of 91.24%, 83.01%, and 84.70%, respectively. The mean absolute error for each component segmentation is 2.95%, 5.43%, and 6.19%, respectively. [Conclusions] The algorithm in this paper has great potential in realizing the use of computer-aided automatic analysis of coal macerals quality.

Key words: coal maceral micro-components, image segmentation, UNet, multiscale contextual attention, squeeze and excitation

季菁菁, 奚峥皓, 李忠峰. 基于MSR-UNet的煤岩显微组分分割与定量分析算法[J]. 数据与计算发展前沿, 2023, 5(6): 126-137.

JI Jingjing, XI Zhenghao, LI Zhongfeng. The Study of Coal Macerals Segmentation and Quantitative Analysis Based on MSR-UNet[J]. Frontiers of Data and Computing, 2023, 5(6): 126-137, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.06.012.

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平台硬件与软件配置
显存	12G	框架	Pytorch1.7
CPU	Intel core i7-7500	Cuda	11.0
内存	2T	语言	Python3.7
GPU	NVIDIA Titan X	操作系统	Windows10 Professional

模型	PA	IoU	Dice
Kmeans	0.7687	0.6713	0.6895
two-step strategy	0.8062	0.7581	0.7614
FCN	0.8570	0.7609	0.7741
SegNet	0.8115	0.7224	0.7376
UNet	0.8863	0.7931	0.8065
MSR-UNet	0.9124	0.8301	0.8470

模型	PA	IoU	Dice
VGG-UNet	0.8987	0.8073	0.8125
Mobile-UNet	0.8962	0.7981	0.8016
Res-UNet	0.9076	0.8179	0.8241

模型	PA	IoU	Dice
Res-UNet	0.9076	0.8179	0.8241
Res-UNet+SE	0.9093	0.8224	0.8376
Res-UNet+MCA	0.9108	0.8267	0.8395
MSR-UNet	0.9110	0.8268	0.8419

L_D	L_F	PA	IoU	Dice
		0.9110	0.8268	0.8419
√		0.9117	0.8271	0.8422
	√	0.9119	0.8273	0.8425
√	√	0.9124	0.8301	0.8470

基于MSR-UNet的煤岩显微组分分割与定量分析算法

The Study of Coal Macerals Segmentation and Quantitative Analysis Based on MSR-UNet

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[2]	赵伟昱,张宏海,仲波. 基于深度学习的遥感影像地块分割方法[J]. 数据与计算发展前沿, 2021, 3(2): 133-141.