ODD-SEC: Onboard Drone Detection with a Spinning Event Camera
Authors: Kuan Dai, Hongxin Zhang, Sheng Zhong, Yi Zhou
Organizations: Neuromorphic Automation and Intelligence Lab (NAIL) at School of Artificial Intelligence and Robotics, Hunan University, Changsha, China.
The rapid proliferation of drones requires balancing innovation with regulation. To address security and privacy concerns, techniques for drone detection have attracted significant attention.Passive solutions, such as frame camera-based systems, offer versatility and energy efficiency under typical conditions but are fundamentally constrained by their operational principles in scenarios involving fast-moving targets or adverse illumination.Inspired by biological vision, event cameras asynchronously detect per-pixel brightness changes, offering high dynamic range and microsecond-level responsiveness that make them uniquely suited for drone detection in conditions beyond the reach of conventional frame-based cameras.However, the design of most existing event-based solutions assumes a static camera, greatly limiting their applicability to moving carriers--such as quadrupedal robots or unmanned ground vehicles--during field operations.In this paper, we introduce a real-time drone detection system designed for deployment on moving carriers. The system utilizes a spinning event-based camera, providing a 360° horizontal field of view and enabling bearing estimation of detected drones. A key contribution is a novel image-like event representation that operates without motion compensation, coupled with a lightweight neural network architecture for efficient spatiotemporal learning. Implemented on an onboard Jetson Orin NX, the system can operate in real time. Outdoor experimental results validate reliable detection with a mean angular error below 2° under challenging conditions, underscoring its suitability for real-world surveillance applications. We will open-source our complete pipeline to support future research.
Figures & tables
Fig. 1: Illustration of our system operating in an outdoor scenario. Top: The scene with the trajectories of the drone (in red) and our device (in blue), with an inset at the bottom-left showing the rear view of the same scene. Bottom: The detection results from our system. Specific measurement locations are marked with circled letters.
Fig. 2: CAD model of the system. Red and green boxes represent the actuation module and the perception-computing module, respectively.
Fig. 3: Flowchart of the proposed algorithm. The two modules are executed independently. The Pre-processing part receives data from the event camera and converts it into image slices. The Detection part receives and processes the image stream, performs network inference, and calculates the relative bearing to the system based on the detection results.
Fig. 4: Overview of the proposed detection network. Our novel Temporal Fusion Module (highlighted in a green dashed border) enhances detection by analyzing a sequence of frames that capture motion relationships. It employs Feature Channel Gating (FCG) to adaptively weight features, aggregates Temporal Context Vector (TCV) across the segment, and fuses it with features from the detection backbone to improve the network’s target recognition accuracy.
Fig. 5: Coordinate systems and bearing estimation. (a) Image plane: Detection result with a red bounding box marking the drone and a dot indicating its centroid. (b) Spinning platform system: The detected centroid is projected into the 3D coordinate system (origin: camera optical center; y-axis: rotation axis; z-axis: zero-direction). This projection enables bearing calculation.
Fig. 6: Illustration of detection results under varying lighting conditions. Top: High-glare scenario with the sun directly within the camera’s FoV during the afternoon. Bottom: Low-light scenario after sunset.
Illumination
AP
AP 75
AR 100
MDS [pixels]
Standard
62.19
69.70
67.12
14
Low
59.30
63.80
62.60
17
High
55.80
58.20
60.20
-
TABLE II: Evaluation under varying lighting conditions [%]. Minimum Detectable Size (MDS) denotes the smallest target width identifiable by the network under the corresponding scenario. In high-glare scenarios, we deem that size-based comparison is not particularly meaningful; therefore, this aspect is not included in the experimental evaluation.
Metric
w/o Temporal Fusion
w/ Temporal Fusion
AP ↑ [%]
62.19
64.92 ( +2.73)
AP 75 ↑ [%]
69.70
72.80 ( +3.10)
AR 100 ↑ [%]
67.12
69.87 ( +2.75)
AP S ↑ [%]
56.30
59.90 ( +3.60)
Params ↓ [M]
8.94
9.53
TABLE III: Ablation study of temporal fusion. Up or down arrows ( ↑ or ↓ ) indicate whether a higher or lower value is better. Network parameters (Params) represent the total number of learnable weights in the network.
Fig. 7: Illustration of the detection results. Left: Trajectories of the carrier and drone, with four representative time instants marked by circled letters and connected by purple dashed lines. Top right: Corresponding camera images showing the detection results, where drone detection results are highlighted. Bottom right: Comparison of the estimated angles (Est.) with the ground truth (GT), with GPS data interpolated using cubic splines to obtain the drone’s position at the exact detection moments.
Sequences
Mean error[°]
Median error[°]
Max error[°]
static_1
2.063
2.096
3.103
static_2
1.772
1.764
2.611
static_3
1.736
1.715
2.835
static_4
1.858
1.848
3.144
moving_1
2.004
1.857
4.340
moving_2
1.927
1.921
4.308
TABLE IV: Evaluation on self-recorded sequences using angular error [°].
Node
Function
Laptop
Jetson NX
Pre-processing
Data processing
0.969
1.938
Image generation
10.841
30.514
Detection
Input construction
3.762
5.223
Inference
11.440
20.425
Bearing estimation
0.0475
0.0823
Others
15.249
18.467
TABLE V: Computational performance on different platforms [ Time : ms].
Centre for Drone Technology Indian Institute of Technology Guwahati Guwahati, India · Dept. of CSE KIIT University Bhubaneswar, India · Drone LAB National Institute of Electronics and Information Technology Bhubaneswar, India