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8 June 2023 Optimization of landslide detection method based on improved mask R-CNN
Shiqi Zhang
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Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072K (2023) https://doi.org/10.1117/12.2680954
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China
Abstract
Aiming at the problems of difficult feature extraction, low detection accuracy and long detection time in traditional landslide detection methods, considering the diversity and complexity of landslide and its background environment, a landslide detection method based on mask region-based convolutional networks (Mask R-CNN) and Squeeze-andExcitation (SE) attention mechanism is proposed. The training set with 2050 samples and the test set with 600 samples were established for landslide detection, which verified the effectiveness of the improved method. The precision, recall and F1-score of the improved recognition are 89.2 %, 98.0 % and 93.4 %, respectively. Precision, recall, and F1-score all rose in comparison to the model before the improvement by 2.9%, 3.9%, and 3.4%, respectively. The findings demonstrate that the enhanced method can effectively increase the accuracy of landslide identification., and the landslide boundary segmentation results are closer to the real landslide boundary, so as to realize automatic and efficient landslide detection.
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Citation Download Citation
Shiqi Zhang "Optimization of landslide detection method based on improved mask R-CNN", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072K (8 June 2023); https://doi.org/10.1117/12.2680954
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KEYWORDS
Network landslides
Image segmentation
Object detection
Education and training
Data modeling
Deep learning
Network architectures
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