Abstract: Landslide area image detection and recognition has rich application and research value in disaster scope recognition, disaster data analysis and disaster prevention and mitigation. In this paper, a target detection method combining attention mechanism CBAM and dilated convolution is proposed to solve the problems of the diversity of landslide body shape and texture in landslide image and the unsatisfactory detection and recognition effect of landslide target area. On the basis of the traditional target detection algorithm Faster R-CNN, the attention mechanism model is added to the convolutional neural network layer. The landslide image features are extracted through the CBAM model combining spatial attention and channel attention, and the dilated convolution module is added to enlarge the receptive field area, and to improve the learning ability of the landslide target recognition and non-standard size in the remote sensing image area of the neural network, so as to further improve the detection accuracy of the landslide target area. The experimental results show that, based on the traditional target detection algorithm, the combination of the two methods can improve the recall rate and precision rate of target detection on the remote sensing images of landslides, and it has a certain validity and robustness.

Key words: landslide, attention mechanism, Faster R-CNN, dilated convolution, target detection