RT Conference Proceedings
T1 SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes.
A1 Salas Espinales, Andrés
A1 Vázquez-Martín, Ricardo
A1 García-Cerezo, Alfonso José
A1 Mandow, Anthony
K1 Aprendizaje automático (Inteligencia artificial)
K1 Visión por ordenador
K1 Catástrofes
AB 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 thatthe attention mechanisms increase model performance while minimally affecting the computation time.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/28175
UL https://hdl.handle.net/10630/28175
LA eng
NO Andrés Salas-Espinales, Ricardo Vázquez-Martı́n, Alfonso Garcı́a-Cerezo, and Anthony Mandow. SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes. Proc. of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) 2023. Fukushima, Japan.
NO 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.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026