Authors:
Antônio Neto
and
Daniel Dantas
Affiliation:
Departamento de Computação, Universidade Federal de Sergipe, São Cristóvão, SE, Brazil
Keyword(s):
Morphological Operations, Registered Images, Damage Level Classification, Unet, BDANet, CutMix.
Abstract:
In this study, our main motivation was to develop and optimize an image segmentation model capable of accurately assessing damage caused by natural disasters, a critical challenge today where the frequency and intensity of these events are increasing. In order to predict damage categories, including no damage, minor damage, and major damage, we compared several models and approaches. we explored and compared several models, focusing on the Unet architecture employing BDANet and other architectures such as ResNet18, VGG16, and ResNet50. Layers with mathematical morphology operations were applied as a filtering strategy. The results indicated that the Unet model with the BDANet backbone had the best performance, with an F1-score of 0.761, which increased to 0.799 after applying mathematical morphology operations.