A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations
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| Pubblicato in: | Technologies vol. 13, no. 1 (2025), p. 35 |
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MDPI AG
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| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Ntousis, Odysseas |u School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece; <email>vmakris@mail.ntua.gr</email> (E.M.); <email>panag@cs.ntua.gr</email> (P.T.) | |
| 245 | 1 | |a A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the workload and the combination of the results produced necessary. In this paper, a dual-stage processing architecture for object detection and tracking in Unmanned Aerial Vehicles (UAVs) is presented, focusing on efficient resource utilization and real-time performance. The proposed system delegates lightweight detection tasks to onboard hardware while offloading computationally intensive processes to a ground server. The UAV is equipped with a Raspberry Pi for onboard data processing, utilizing an Intel Neural Compute Stick 2 (NCS2) for accelerated object detection. Specifically, YOLOv5n is selected as the onboard model. The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. The proposed architecture ensures real-time tracking with minimal latency, making it suitable for mission-critical UAV applications such as surveillance and search and rescue. The results demonstrate the system’s robustness in various environments, highlighting its potential for effective object tracking under limited onboard computational resources. The system achieves recall and accuracy scores as high as 0.53 and 0.74, respectively, using the remote server, and is capable of re-identifying a significant portion of objects of interest lost by the onboard system, measured at approximately 70%. | |
| 653 | |a Computer architecture | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Sensors | ||
| 653 | |a Network latency | ||
| 653 | |a Onboard data processing | ||
| 653 | |a Multiple target tracking | ||
| 653 | |a Tracking | ||
| 653 | |a Resource utilization | ||
| 653 | |a Queries | ||
| 653 | |a Kalman filters | ||
| 653 | |a Real time | ||
| 653 | |a Vehicles | ||
| 653 | |a Search and rescue missions | ||
| 700 | 1 | |a Makris, Evangelos |u School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece; <email>vmakris@mail.ntua.gr</email> (E.M.); <email>panag@cs.ntua.gr</email> (P.T.) | |
| 700 | 1 | |a Tsanakas, Panayiotis |u School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece; <email>vmakris@mail.ntua.gr</email> (E.M.); <email>panag@cs.ntua.gr</email> (P.T.) | |
| 700 | 1 | |a Pavlatos, Christos |u Digital Development Technologies (DDTech) P.C., 59c Evdomi St., P. Fokaia, 19013 Athens, Greece; <email>christos.pavlatos@hafa.haf.gr</email>; Hellenic Air Force Academy, Dekelia Air Base, Acharnes, 13671 Athens, Greece | |
| 773 | 0 | |t Technologies |g vol. 13, no. 1 (2025), p. 35 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159556561/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159556561/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159556561/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |