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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| Vydáno v: | 2024 International Conference on New Trends in Computational Intelligence (NTCI) s. 533 - 537 |
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IEEE
18.10.2024
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Ju surname: Wang fullname: Wang, Ju email: superjonwj@126.com organization: College of Computer Science and Technology, Yangtze University,Hubei,China – sequence: 2 givenname: Senlin surname: Yin fullname: Yin, Senlin email: yinxiang_love@126.com organization: Research Institute of Mud Logging Technology and Engineering, Yangtze University,Hubei,China – sequence: 3 givenname: Zhangming surname: Hu fullname: Hu, Zhangming email: huzhmkl@cnpc.com.cn organization: Xibu Drilling Engineering Company Limited,Engineering and Technology Division of CNPC – sequence: 4 givenname: Kai surname: Bai 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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| 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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