An Identification Method for Mixed Coal Vitrinite Components Based on An Improved DeepLabv3+ Network

Fujie Wang, Fanfan Li, Wei Sun, Xiaozhong Song and Huishan Lu ()
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Fujie Wang: School of Mechanical Engineering, North University of China, Taiyuan 030051, China
Fanfan Li: School of Mechanical Engineering, North University of China, Taiyuan 030051, China
Wei Sun: School of Mechanical Engineering, North University of China, Taiyuan 030051, China
Xiaozhong Song: Xi’an Research Institute of China Coal Research Institute, Xi’an 710054, China
Huishan Lu: School of Mechanical Engineering, North University of China, Taiyuan 030051, China

Energies, 2024, vol. 17, issue 14, 1-16

Abstract: To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module to better filter out important semantic features. Then, a corrective convolution module is added to the network’s output to ensure the consistency of each channel’s output feature map for each type of vitrinite. Finally, images of 14 single vitrinite components are used as training samples for network training, and a validation set is used for identification testing. The results show that the improved DeepLabv3+ achieves 6.14% and 3.68% improvements in MIOU (mean intersection over union) and MPA (mean pixel accuracy), respectively, compared to the original DeepLabv3+; 12% and 5.3% improvements compared to U-Net; 9.26% and 4.73% improvements compared to PSPNet with ResNet as the backbone; 5.4% and 9.34% improvements compared to PSPNet with MobileNetV2 as the backbone; and 6.46% and 9.05% improvements compared to HRNet. Additionally, the improved ASPP module increases MIOU and MPA by 3.23% and 1.93%, respectively, compared to the original module. The CBAM attention mechanism with a channel multiplier of 8 improves MIOU and MPA by 1.97% and 1.72%, respectively, compared to the original channel multiplier of 16. The data indicate that the proposed identification method significantly improves recognition accuracy and can be effectively applied to mixed coal vitrinite identification.

Keywords: vitrinite; DeepLabv3+; MobileNetV2; ASPP; CBAM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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