A street view image change detection method combining semantic segmentation model and graph cuts

Due to the problems of the diversity of the scale, the unclear boundary and the complex spectral information of the ground objects, the performance of the statistical method and machine learning method for the change detection of the high complexity street view images is poor. Therefore, a street view images change detection method combining semantic segmentation model and graph cuts (GC) is proposed in this paper. Firstly, the DeeplabV3+ semantic segmentation model combined with migration learning is used to pre-train the Camvid data set to obtain the pre-trained model in this method; Then a small number of annotated samples from the data set of this paper were used to Fine Tune the pre-training model, which was respectively used for semantic segmentation of street view images in two periods. Then GC method is used to remove the sky, roads,vegetation and other factors, which is impacting on the street view. Finally, change vector analysis (CVA) is used to obtain the difference images, and binarization and accuracy evaluation were carried out for the difference images. The results show that the overall accuracy of the proposed method is better than the Otsu method (OTSU), K-means method, Segnet network migration learning method and DeeplabV3+ network migration learning method, it is a feasible method for detecting changes in street view images.