Debris Flow Gully Identification Based on Improved ResNet50 Network
Traditional convolutional neural networks have problems such as low accuracy,poor image feature extraction and blurred edges when used for landslide disaster valley image classification.This paper improves the ResNet50 network by adding a CBAM attention mechanism module before some residual blocks of the ResNet50 network,which enables it to have higher performance and accuracy and accurately capture the terrain and landforms in landslide disaster valley images.The experimental results show that the improved ResNet50 network achieves a classification accuracy of 83.02％for landslide disaster valley images,which improves its classification performance by 11.32 percentage points compared to the ResNet50 network.Moreover,its accuracy,recall rate,precision rate,F1 value,and AUC value are better than those of the ResNet50 network and other deep learning recognition algorithms.
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