Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire

• URL: http://arxiv.org/abs/2509.12997v1
• Date: Tue, 16 Sep 2025 12:08:03 GMT
• Title: Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire
• Authors: Anton Eldeborg Lundin, Rasmus Winzell, Hanna Hamrell, David Gustafsson, Hannes Ovrén,
• Abstract summary: Small drones are an increasing threat to both military personnel and civilian infrastructure.<n>We develop a system that uses spiking neural networks and neuromorphic cameras to detect drones.
• Score: 3.697194144254579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small drones are an increasing threat to both military personnel and civilian infrastructure, making early and automated detection crucial. In this work we develop a system that uses spiking neural networks and neuromorphic cameras (event cameras) to detect drones. The detection model is deployed on a neuromorphic chip making this a fully neuromorphic system. Multiple detection units can be deployed to create a virtual tripwire which detects when and where drones enter a restricted zone. We show that our neuromorphic solution is several orders of magnitude more energy efficient than a reference solution deployed on an edge GPU, allowing the system to run for over a year on battery power. We investigate how synthetically generated data can be used for training, and show that our model most likely relies on the shape of the drone rather than the temporal characteristics of its propellers. The small size and low power consumption allows easy deployment in contested areas or locations that lack power infrastructure.

Related papers

• Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW [52.280742520586756]
  Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. We present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones.
  arXiv  Detail & Related papers  (2024-08-06T13:11:36Z)
• Obfuscated Location Disclosure for Remote ID Enabled Drones [57.07815312703613]
  We propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID)<n>Instead of disclosing the actual drone's location, drones equipped with OLO-RID disclose a differentially private obfuscated location in a mobile scenario.<n>OLO-RID also extends RID messages with encrypted location information, accessible only by authorized entities.
  arXiv  Detail & Related papers  (2024-07-19T12:35:49Z)
• C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks [23.133250476580038]
  A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. detecting drones presents unique challenges, including small object sizes, distortion, and real-time processing requirements. We propose a novel coarse-to-fine detection strategy based on vision transformers.
  arXiv  Detail & Related papers  (2024-04-30T05:51:21Z)
• High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks [51.23613834703353]
  Relative drone-to-drone localization is a fundamental building block for any swarm operations. We present a vertically integrated system based on a novel vision-based fully convolutional neural network (FCNN) Our model results in an R-squared improvement from 32 to 47% on the horizontal image coordinate and from 18 to 55% on the vertical image coordinate, on a real-world dataset of 30k images.
  arXiv  Detail & Related papers  (2024-02-21T12:34:31Z)
• Fully neuromorphic vision and control for autonomous drone flight [5.358212984063069]
  Event-based vision and spiking neural hardware promises to exhibit similar characteristics. Here, we present a fully learned neuromorphic pipeline for controlling a drone flying. Results illustrate the potential of neuromorphic sensing and processing for enabling smaller network per flight.
  arXiv  Detail & Related papers  (2023-03-15T17:19:45Z)
• Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs [69.19616451596342]
  Miniaturized unmanned aerial vehicles (UAVs) are an emerging and trending topic. We leverage a novel neural architecture search (NAS) technique to automatically identify several convolutional neural networks (CNNs) for a visual pose estimation task. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of 10Hz@10mW and 50Hz@90mW.
  arXiv  Detail & Related papers  (2023-03-03T14:02:09Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• DronePose: The identification, segmentation, and orientation detection of drones via neural networks [3.161871054978445]
  We present a CNN using a decision tree and ensemble structure to fully characterise drones in flight. Our system determines the drone type, orientation (in terms of pitch, roll, and yaw), and performs segmentation to classify different body parts. We also provide a computer model for the rapid generation of large quantities of accurately labelled photo-realistic training data.
  arXiv  Detail & Related papers  (2021-12-10T12:34:53Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.