A method for identifying gully-type debris flows based on adaptive multi-scale feature extraction

Abstract

Rapid and accurate identification of gully-type debris flows is vital for safeguarding lives and property in mountainous regions. To address the issues of inaccurate and insufficient gully feature extraction, we propose a dual-branch feature extraction module based on multi-scale and self-attention mechanisms. Integrated into the feature layer of Convolutional Neural Network (CNN) to solve the multi-scale feature extraction problem of gullies. First, the feature extraction component consists of a dual-branch structure with a global feature extraction part based on self-attention mechanisms and a local feature extraction part based on multi-scale methods, designed to extract gully features at different scales and establish connections among them. Next, during the multi-scale feature map output stage, feature maps from different scales are fused by adaptively updating their weights. Finally, a multi-layer perceptron classifies gully images. Results indicate that the proposed module effectively extracts feature at different scales, accurately representing the intrinsic characteristics of gullies. When integrated with traditional CNNs, the module significantly improves the recognition performance of gully-type debris flows. For instance, with ResNet18, the model achieves an accuracy of 89.7% and recall of 85.7%, representing improvements of 17.3% and 21.4%, respectively, over the baseline.

Keywords

• Self-attention mechanism
• adaptive feature fusion module
• debris flow disasters
• multi-scale feature extraction
• remote sensing