Fast and accurate observation of an area in disaster scenarios such as earthquake, flood and avalanche is crucial for first aid teams. Digital surface models, orthomosaics and object detection algorithms can play an important role for rapid decision making and response in such scenarios. In recent years, Unmanned Aerial Vehicles (UAVs) have become increasingly popular because of their ability to perform different tasks at lower costs. A real-time orthomosaic generated by using UAVs can be helpful for various tasks where both speed and efficiency are required. An orthomosaic provides an overview of the area to be observed, and helps the operator to find the regions of interest. Then, object detection algorithms help to identify the desired objects in those regions. In this study, a monocular SLAM based system, which combines the camera and GPS data of the UAV, has been developed for mapping the observed environment in real-time. A deep learning based state-of-the-art object detection method is adapted to the system in order to detect objects in real time and acquire their global positions. The performance of the developed method is evaluated in both single and multiple UAVs scenarios.

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