This paper evaluates four semantic segmentation models in Search-and-Rescue (SAR) scenarios obtained from ground vehicles. Two base models are used (U-Net and PSPNet) to compare different approaches to semantic segmentation, such as skip connections between encoder and decoder stages and using a pooling pyramid module. The best base model is modified by including two attention mechanisms to analyze their performance and computational cost. We conduct a quantitative and qualitative evaluation using our SAR dataset defining eleven classes in disaster scenarios. The results demonstrate that
the attention mechanisms increase model performance while minimally affecting the computation time.This work has been partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project PID2021-122944OB-I00.
Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
