期刊
SENSORS
卷 21, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/s21217346
关键词
robotic systems; thermal images; convolutional neural networks; computer vision; search and rescue robots; ROS; Unitree A1
资金
- Programas de Actividades I+D en la Comunidad Madrid [S2018/NMT-4331]
- EU
- TASAR (Team of Advanced Search And Rescue Robots) - Proyectos de I+D+i del Ministerio de Ciencia, Innovacion y Universidades [PID2019-105808RB-I00]
Recent technological breakthroughs have revolutionized fields such as machine vision and search and rescue robotics, with the application of new and improved neural networks combined with modern optical sensors like thermal cameras for victim identification in post-disaster environments. Robotic teams equipped with specific sensors have been developed for primary intervention tasks in high-risk post-disaster environments. Through testing and validation, a novel automatic method utilizing thermal image processing and CNN has shown over 90% efficiency in victim identification.
Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper's main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot's automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method's efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.
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