RT Conference Proceedings
T1 Disaster area recognition from aerial images with complex-shape class detection.
A1 González-Navarro, Rubén
A1 Lin-Yang, Da-hui
A1 Vázquez-Martín, Ricardo
A1 García-Cerezo, Alfonso José
K1 Catástrofes
K1 Visión por ordenador
K1 Aviones sin piloto
K1 Redes neuronales (Informática)
AB This paper presents a convolutional neural network (CNN) model for event detection from Unmanned Aerial Vehicles (UAV) in disaster environments. The model leverages the YOLOv5 network, specifically adapted for aerial images and optimized for detecting Search and Rescue (SAR) related classes for disaster area recognition. These SAR-related classes are person, vehicle, debris, fire, smoke, and flooded areas. Among these, the latter four classes lead to unique challenges due to their lack of discernible edges and/or shapes in aerial imagery, making their accurate detection and performance evaluation metrics particularly intricate. The methodology for the model training involves the adaptation of the pre-trained model for aerial images and its subsequent optimization for SAR scenarios. These stages have been conducted using public datasets, with the required image labeling in the case of SAR-related classes. An analysis of the obtained results demonstrates the model’s performance while discussing the intricacies related to complex-shape classes. The model and the SAR datasets are publicly available.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/28176
UL https://hdl.handle.net/10630/28176
LA eng
NO Rubén González-Navarro, Dahui Lin-Yang, Ricardo Vázquez-Martı́n, and Alfonso Garcia-Cerezo. Disaster area recognition from aerial images with complex-shape class detection. Proc. of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) 2023. pp. 126-131. Fukushima, Japan.
NO This work has been partially funded by the Spanish Ministerio deCiencia, Innovación y Universidades, Gobierno de España, project PID2021-122944OB-I00.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026