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Lithology Identification of UAV Oblique Photography Images Based on Semantic Segmentation Neural Network Algorithm.
- Source :
-
Mathematical Geosciences . Jul2024, Vol. 56 Issue 5, p1053-1072. 20p. - Publication Year :
- 2024
-
Abstract
- Intelligent identification of the different lithology of outcrops has been a major research challenge in the field of geology. The manual identification of outcrop lithology is labor-intensive and lacks systematicity and consistency. Intelligent lithology identification based on deep convolutional neural networks can significantly reduce such workload and also serves as the foundation for intelligent outcrop research. In this paper, we propose a workflow based on the DeepLabV3+ semantic segmentation neural network algorithm. The algorithm can intelligently identify the lithology of outcrop photos obtained by unmanned aerial vehicle (UAV) oblique photography. Based on this workflow, we can complete the tasks of dataset creation, model training, intelligent identification of outcrop lithology, and model accuracy evaluation. The dataset used in this paper is composed of high-resolution photos of the Fugu Tiansheng Bridge Shihezi Formation profile collected by UAV, which are annotated with three major categories and five subcategories of lithology. Then, we input the data into four types of neural networks, including a fully convolutional network (FCN), SegNet, U-Net, and DeepLabV3+, for training. The results indicate that the recognition accuracies of the four neural network algorithms, FCN, SegNet, U-Net, and DeepLabV3+, for outcrop lithology classification tasks based on UAV oblique photography images are 90.81%, 90.44%, 92.03%, and 93.88%, respectively. Among such algorithms, the DeepLabV3+ semantic segmentation neural network algorithm is reliable and accurate, and can provide a basis for intelligent lithology recognition. This is fundamental for promoting the automation, intelligence, and digitalization of geological outcrop research. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18748961
- Volume :
- 56
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Mathematical Geosciences
- Publication Type :
- Academic Journal
- Accession number :
- 178402336
- Full Text :
- https://doi.org/10.1007/s11004-023-10108-3