Object Detection from Thermal Infrared and Visible Light Cameras in Search and Rescue Scenes
| Autor: | R. Vazquez-Martin, Adrian Banuls, Alfonso García-Cerezo, Anthony Mandow, Jesus Morales |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Rescue robot
Synthetic aperture radar Search and rescue Computer science business.industry Deep learning Cognitive neuroscience of visual object recognition 02 engineering and technology 01 natural sciences Convolutional neural network Object detection 010309 optics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering RGB color model Detectores de infrarrojos 020201 artificial intelligence & image processing Computer vision Artificial intelligence Thermal infrared business Robots Disaster robotics |
| Zdroj: | SSRR RIUMA. Repositorio Institucional de la Universidad de Málaga instname RIUMA: Repositorio Institucional de la Universidad de Málaga Universidad de Málaga |
| ISSN: | 2018-0934 |
| DOI: | 10.1109/ssrr50563.2020.9292593 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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