Methods for landslide detection based on lightweight YOLOv4 convolutional neural network

The rapid and accurate positioning of the landslides through remote sensing data plays an important role in post-disaster emergency rescue. This paper was proposed a new algorithm for landslide detection in the plateau environment. The YOLOv4 was used as the basic framework, and the MobileNetv3 model was utilized as the feature extraction network to replace the backbone neural network CSPdarknet53 which was to improve the efficiency of landslide detection. By applying depth separable convolution, the parameters of the model are decreasing significantly. To further improve the accuracy of landslide detection, the coordinate attention mechanism was introduced in the bottleneck. 3070 landslide images in the Linzhi area from 2010 to 2019 were obtained through Google Earth to train and test the model. On this basis, we compared the detection speed and accuracy of other single-stage and two-stage target detection algorithms in landslide detection. Moreover, the performances of the model were analyzed under the different attention mechanisms. The results show that our model can reduce the number of parameters by 83.59% compared with the YOLOv4 model. The accuracy of landslide detection by the model is improved to 91.2%, and the detection rate reaches 35f/s. It means that the model proposed in this study would provide useful information and rapid detection for hazard assessment and emergency rescue.

Automated summary

This paper proposes a new algorithm for landslide detection in plateau environment using remote sensing data. The algorithm utilizes YOLOv4 framework and MobileNetv3 model to improve the efficiency and accuracy of landslide detection. The results show that the proposed model can significantly reduce the number of parameters while achieving high accuracy and detection speed.