Autonomous real-time drone-to-drone visual detection by onboard hardware platform

The proliferation of unmanned aerial vehicles (UAVs) in both civilian and military domains has intensified the need for autonomous counter-drone systems capable of operating without reliance on ground infrastructure. Existing ground-based and hybrid approaches suffer from high latency and complete failure under communication jamming or denial. This paper proposes a fully onboard (OB) architecture for autonomous drone-to-drone detection in both the visible (RGB) and thermal (IR) domains, where all perception and decision-making tasks are executed exclusively using the embedded computational resources of the unmanned aerial vehicle. In particular, this work analyzes the features of the physical components of the architecture (i.e., the Single-Board Computers or SBCs for short, the available sensors, and the UAV platforms) and their performances in the experimental settings. Various computational platforms are tested to assess their impact on the performance of the detection pipeline, evaluating specific parameters such as inference speed (fps), inference time (ms), power consumption (W) and operational autonomy. In order to enable a comprehensive evaluation, a ground-based (GB) counterpart was also implemented, where real-time video streams are transmitted from the drone to a ground station for processing and control commands are subsequently sent back. The onboard architecture offers significantly lower latency and complete independence from radio links and controllers, making it particularly suitable for applications requiring high robustness in communication-denied or contested environments. In particular, the findings highlight the advantages of the Jetson Orin Nano platform in achieving inference speeds up to 80.93 fps at 12.36 ms on YOLO v8n quantized models, overcoming state-of-the-art performances. According to our knowledge, this is the first fully onboard RGB-IR drone-to-drone visual detection architecture in the literature.