Lithology Identification of Improved Model based on DeepLabv3 + Deep Convolutional Neural Network

Aiming at the complexity of outcrop lithology identification, an improved outcrop lithology identification algorithm based on DeepLabv3 + is proposed. We use the Xception network replaced by the MobileNetV2 module to enhance the ability to extract lithologic features and make the model more lightwei...

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Published in:2024 International Conference on New Trends in Computational Intelligence (NTCI) pp. 533 - 537
Main Authors: Wang, Ju, Yin, Senlin, Hu, Zhangming, Bai, Kai
Format: Conference Proceeding
Language:English
Published: IEEE 18.10.2024
Subjects:
Accuracy
Attention mechanism
Attention mechanisms
Classification algorithms
Complexity theory
Computational intelligence
Computational modeling
Convolutional neural networks
Feature extraction
lightweight model
lithology identification
Semantic segmentation
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Abstract Aiming at the complexity of outcrop lithology identification, an improved outcrop lithology identification algorithm based on DeepLabv3 + is proposed. We use the Xception network replaced by the MobileNetV2 module to enhance the ability to extract lithologic features and make the model more lightweight. In order to segment pixels more accurately, the CA (Coordinate attention) attention mechanism is introduced, so that the improved backbone network can extract lithologic features more accurately in complex environments. The experimental results show that compared with the traditional DeepLabv3 +, the accuracy of the improved algorithm is increased by 1.08 percentage points, the MP A is increased by 2.4 percentage points, and the average classification accuracy is 94.86 %. Compared with other mainstream semantic segmentation models, all aspects of performance are more prominent.
AbstractList Aiming at the complexity of outcrop lithology identification, an improved outcrop lithology identification algorithm based on DeepLabv3 + is proposed. We use the Xception network replaced by the MobileNetV2 module to enhance the ability to extract lithologic features and make the model more lightweight. In order to segment pixels more accurately, the CA (Coordinate attention) attention mechanism is introduced, so that the improved backbone network can extract lithologic features more accurately in complex environments. The experimental results show that compared with the traditional DeepLabv3 +, the accuracy of the improved algorithm is increased by 1.08 percentage points, the MP A is increased by 2.4 percentage points, and the average classification accuracy is 94.86 %. Compared with other mainstream semantic segmentation models, all aspects of performance are more prominent.
Author Hu, Zhangming
Wang, Ju
Yin, Senlin
Bai, Kai
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  fullname: Bai, Kai
  email: 30817446@qq.com
  organization: College of Computer Science and Technology, Yangtze University,Hubei,China
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Snippet Aiming at the complexity of outcrop lithology identification, an improved outcrop lithology identification algorithm based on DeepLabv3 + is proposed. We use...
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StartPage 533
SubjectTerms Accuracy
Attention mechanism
Attention mechanisms
Classification algorithms
Complexity theory
Computational intelligence
Computational modeling
Convolutional neural networks
Feature extraction
lightweight model
lithology identification
Semantic segmentation
Title Lithology Identification of Improved Model based on DeepLabv3 + Deep Convolutional Neural Network
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