Human Detection and Localization in Indoor Disaster Environments Using UAVs

Rizvi, Syed Muhammad Ali; Ahmed, Rameen Mobin; Alamdar, Khawaja Ghulam; Khorasani, Mehdi Raza; Memon, Junaid Ahmed

doi:10.1109/icrcv55858.2022.9953174

Cited by 3 publications

(1 citation statement)

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“…Similarly, for assessing building damages, authors [12]- [14] propose a CNN to detect roofs from UAV images and evaluate the extent of the damage. Also, CNN is used to search people with only RGB images, as it is done in [15], and by incorporating thermal infrared information for people detection [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Disaster Area Recognition from Aerial Images with Complex-Shape Class Detection

González-Navarro,

Lin-Yang,

Vázquez-Martín

et al. 2023

2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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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 SARrelated 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.

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