SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes. - Salas Espinales, Andrés; Vázquez-Martín, Ricardo; García-Cerezo, Alfonso José; Mandow, Anthony (My University, 2023)
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.
Keywords: Aprendizaje automático (Inteligencia artificial); Visión por ordenador; Catástrofes; Deep Learning; Semantic Segmentation; Attention Mechanism; Disaster Robotics