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| Title | Accuracy-Versus-Energy Evaluation In Drone-Based Video Processing For Object Detection |
|---|---|
| ID_Doc | 6014 |
| Authors | Bisio I.; Haleem H.; Garibotto C.; Lavagetto F.; Sciarrone A. |
| Year | 2022 |
| Published | Proceedings - IEEE Global Communications Conference, GLOBECOM |
| DOI | http://dx.doi.org/10.1109/GLOBECOM48099.2022.10000961 |
| Abstract | Drones and video processing have become a vital and integrated aspect of smart city development in several applications such as search and rescue, surveillance and delivery. Newly, camera-equipped drones are flown, and high-resolution photos and video data are relayed via a communication link to the base station. Apart from tackling the video processing issues in applications, such as object identification and tracking, energy consumption that may present a stumbling block in completing a successful drone flight for data collecting must be highlighted and considered. Drones have a limited amount of energy storage, which must be used to power the drone's movement, hovering, data collection, and communication. This study aims at building and testing a drone energy profile that estimates the total energy consumed and a maximum flying time of a drone in a traffic monitoring scenario. Additionally, the relationship between the video processing task and the drone energy profile is explored to determine the optimal strategy for flying the drones while maintaining the video processing task's performance. The conclusion of this evaluation and investigation can be conceived of as the test-case scenario to fly a drone for surveillance and monitoring applications to attain the optimum results. © 2022 IEEE. |
| Author Keywords | communication; deep learning; detection; drone; energy profile; video processing |
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