RT Conference Proceedings
T1 Object Detection from Thermal Infrared and Visible Light Cameras in Search and Rescue Scenes
A1 Bañuls, Adrián
A1 Mandow, Anthony
A1 Vázquez-Martín, Ricardo
A1 Morales-Rodríguez, Jesús
A1 García-Cerezo, Alfonso José
K1 Robots
K1 Detectores de infrarrojos
AB Visual object recognition is a fundamental challenge for reliable search and rescue (SAR) robots, where vision can be limited by lighting and other harsh environmental conditions in disaster sites.  The goal of this paper is to explore the use of thermal and visible light images for automatic object detection in SAR scenes.  With this purpose, we have used a new dataset consisting of pairs of thermal infrared (TIR) and visible (RGB) video sequences captured from an all-terrain vehicle moving through several realistic SAR exercises participated by actual first response organisations. Two instances of the open source YOLOv3 convolutional neural network (CNN) architecture are trained from annotated sets of RGB and TIR images, respectively.  In particular, frames are labelled with four representative classes in SAR scenes comprising both persons civilian and first-responder) and vehicles (Civilian-car and response-vehicle). Furthermore, we perform a comparative evaluation of these networks that can provide insight for future RGB/TIR fusion.
PB IEEE
YR 2020
FD 2020-11
LK https://hdl.handle.net/10630/20415
UL https://hdl.handle.net/10630/20415
LA eng
NO Bañuls, A., Mandow, A., Vázquez-Martín, R. Morales, J., García-Cerezo, A., "Object Detection from Thermal Infrared and Visible Light Cameras in Search and Rescue Scenes", En: IEEE  International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp 1-7. 2020.
NO This work has been done in the framework of the TRUST-ROB project, funded by the Spanish Government (RTI2018-093421-B-I00).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026