cs.ROSep 17, 2026

Project SCOUT: Interceptor Drone for Perimeter Defense

Authors: Azmain Yousuf, Siwei Cai, Knut Peterson, Lifeng Zhou, David Han

Organizations: Drexel University

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Abstract

The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for perimeter defense. This paper presents SCOUT (Spatial Computation for Optimized UAV Tracking), a ROS-integrated onboard perception and control framework for real-time aerial defense against incoming UAVs. SCOUT performs visual detection, target association, track filtering, and control command generation directly onboard the defender UAV, without relying on external sensing infrastructure or ground-station computation. To provide stable control inputs, the perception pipeline combines TensorRT-accelerated drone detection with ByteTrack-based association and a lightweight track-retention state machine. The state machine rejects abrupt target jumps and maintains short-term target continuity during temporary detection degradation, reducing unstable control responses caused by false detections or target switching. We evaluate the proposed architecture through an integrated hardware deployment executing a planar ``goalkeeping'' interception strategy. In this setting, the defender UAV tracks the incoming target and adjusts its motion to maintain a blocking configuration near the protected boundary. Real-world flight results show that SCOUT maintains valid target detections for 92.2% of frames while operating at real-time onboard detection rates, demonstrating the feasibility of visual tracking and closed-loop control for UAV perimeter defense. A video demonstration of the end-to-end perimeter defense operation is available online. https://figshare.com/s/497befc033091fc0b84f

Figures & tables

Fig. 1: SCOUT Visual Perception Pipeline: Images from the defender UAV are processed by a TensorRT-accelerated detection model on the Jetson Orin NX. The resulting drone detections are temporally associated and geometrically filtered before being converted into velocity commands for closed-loop interception control.
Fig. 2: First-person view from the defender UAV. The detected target UAV is outlined in green, and the image-plane target-center offsets used for control are shown by the red and yellow markers.
Fig. 3: Invader Drone: DJI Neo (left) and Defender Drone: Custom Built (right)
Fig. 4: Overhead view of the indoor perimeter-defense test setup. The defender UAV operates near the protected boundary, while the manually piloted target UAV approaches from the attack side.
Model VariantmAP@50mAP@50-95
YOLO26n (pretrained)0.8780.600
YOLO26n (fine-tuned)0.9550.824
TABLE I: YOLO26n model performance for DJI Neo drone detection.
ConfigurationTotal FramesDetectionsFalse Positives
YOLO (Baseline)1,8271,19530
YOLO + ByteTrack1,8271,19530
YOLO + ByteTrack + Filter1,8271,1640
TABLE II: Ablation Study of Tracking and Filtering Components
Fig. 5: Closed-loop image-center tracking performance during the active defense phase. The top plot shows the horizontal and vertical tracking errors in the control frame, while the bottom plot shows the corresponding issued lateral and vertical control commands. The defender maintains visual lock with RMSE values of 0.1340.134 horizontally and 0.1070.107 vertically in normalized image coordinates.
MetricValue
Operation duration104.53 s
Total messages3326
Average publish Frame rate31.82 FPS
Median message gap30.29 ms
Maximum message gap100.72 ms
Missing frames29
TABLE III: Detection-stream performance during the representative real-world flight.
 PyTorch (ms)TensorRT (ms)
StageMeanMaxMeanMax
Worker wake / image handoff7.218.15.413.7
Preprocessing4.15.84.86.3
Inference26.133.67.910.6
NMS + ByteTrack association7.112.32.510.8
Outlier filtering<0.1<0.10.1<0.1<0.1<0.1<0.1
TABLE IV: Detector-side latency comparison: PyTorch vs. TensorRT on Jetson Orin NX.
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