Performance Evaluation of Optimizers for Deformable-DETR in Natural Disaster Damage Assessment

Abstract

Global natural disasters are becoming more frequent and severe as a result of climate change. Recent advances in computer vision, particularly deep learning-based techniques and unmanned aerial vehicle (UAV) remote sensing, can aid disaster response teams in assessing the damage. Prior methods appear to be ineffective or were designed with inductive biases, making them difficult to conduct during the disaster damage assessment. In this paper, we investigate deep-learning-based methods capable of rapidly assessing building damage that follows natural disasters. Furthermore, we examine Deformable DETR, which is an improvement upon DETR, an object detection method based on the Transformer architecture, in terms of efficiency and convergence time, while inheriting DETR’s simple implementation and adaptable architecture, making it suitable for the task of damage detection. We also experimented and analyzed the performance of several optimizers to improve the performance of Deformable DETR.