Improving the efficiency of damaged buildings detection based on ASPP technologies

Abstract

The paper presents an increase in the efficiency of detecting damaged buildings on satellite images of the U-Net convolutional network model by modifying. Instead of the usual bottleneck, the use of atrous spatial pyramid pooling (ASPP) is proposed. As part of the study, the dataset was expanded to 100 images with dimensions of 512x512 pixels, and various augmentations were applied to increase the variability of the dataset, which contributed to more effective training on a limited dataset. Weighting coefficients for each image were also added to the dataset, which were used during training to solve the problem of the predominance of the number of pixels of one class over others. Models of different configurations with an ASPP layer were built and compared with the base U-Net model without ASPP. As a result of testing on the evaluation dataset, an increase in the mean IoU by 5.39% compared to the classical architecture was observed, as well as a significant reduction in overall losses and an increase in the mean IoU by about 2% on a separate testing dataset, which indicates a corresponding increase in the model's efficiency. The proposed architecture can be used in further studies of segmentation of images of buildings damaged by hostilities.