2026-05-27T05:35:37Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/281762026-02-03T11:54:35Zcom_10630_2254col_10630_37959

Disaster area recognition from aerial images with complex-shape class detection. González-Navarro, Rubén Lin-Yang, Da-hui Vázquez-Martín, Ricardo García-Cerezo, Alfonso José Catástrofes Visión por ordenador Aviones sin piloto Redes neuronales (Informática) 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. 2023-11-29T12:57:48Z 2023-11-29T12:57:48Z 2023 2023 conference output 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. https://hdl.handle.net/10630/28176 eng IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) 2023 Fukushima, Japan. 11/2023 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional