Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning
ORCID
https://orcid.org/0000-0002-4237-6327
Document Type
Book Part
Disciplines
Electrical and Computer Engineering | Engineering | Mechanical Engineering
Abstract
Victim detection in smoky indoor environments during search and rescue missions is still challenging and a critical situation. This situation is because firefighters are on the one hand exposed to unstable building structures and on the other hand their cognitive fatigue, due to long search missions, reduces the efficient victim detection in these hazardous environments. In this paper, an approach to detect a victim in real time with an optical and low resolution thermal camera assisting firefighters in their missions is presented. Thereby, the multi-sensor unit is mounted on a remote-controlled mobile robot with a trained victim detector using deep learning and display the detection in real time to an operator outside the scene. Experiments show that this approach enables an efficient detection in smoky indoor environments. The victim detection model achieves an average detection rate above 75% in real time with a low resolution thermal camera in dense smoke.
Recommended Citation
Gelfert, S. (2023). Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning. In: , et al. Robot Intelligence Technology and Applications 7. RiTA 2022. Lecture Notes in Networks and Systems, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-031-26889-2_32
Publication Details
Robot Intelligence Technology and Applications 7. RiTA 2022. Lecture Notes in Networks and Systems, vol 642. © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.