Towards a Semi-Autonomous Robot Platform for the Characterisation of Radiological Environments

Schepper, David De; Dekker, Ivo; Simons, Mattias; Brabants, Lowie; Schroeyers, Wouter; Demeester, Eric

doi:10.1109/ssrr56537.2022.10018668

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…These scenarios face challenges such as hazardous environments, access limitations, and time constraints. Disaster robotics has emerged in recent decades to increase the capabilities of human rescue teams in tasks such as victim search and assistance [1]- [3] or reduce the risk of first-responders while on duty [4], [5]. Detecting and identifying distinct objects, structures, and potential hazards in complex disaster situations in terrain scenes is a valuable capability for victim search, risk management, and rescue team organization in a Search and Rescue (SAR) mission.…”

Section: Introductionmentioning

confidence: 99%

SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes

Salas-Espinales,

Vázquez-Martín,

García-Cerezo

et al. 2023

2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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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.

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