Remote sensing landslide target detection method based on improved Faster R-CNN

To timely detect landslide hazards to start emergency rescue, an improved Faster R-CNN algorithm is proposed for remote sensing image landslide detection. First, the gamma transform and Gaussian filtering methods of image enhancement are used to improve the quality of the images. Second, the effect of batchsize size on the model is eliminated using the group normalization method. Finally, multiscale feature fusion is performed by adding a feature pyramid network structure to optimize the extracted landslide small target features, and then the backbone network is set as deep residual shrinkage network 50 to make the model more focused on information useful for landslide detection. The experimental results show that the improved model improves the accuracy rate as well as the average precision by 8.8% and 8.4%, respectively, compared with the unimproved Faster R-CNN, and compared with the first-stage models, such as you only look once version 4 and single-shot detector, which verify the superiority of the model in our study and can detect landslide targets well.