# Real-Time Sense and Detect of Drones Using Deep Learning and Airborne LiDAR

arXiv: https://arxiv.org/abs/2310.09589 · HTML: https://mlsanity.com/paper/2310.09589/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [Airborne Neural Network](https://mlsanity.com/paper/2505.24513/) (2025)
- [SSCATeR: Sparse Scatter-Based Convolution Algorithm with Temporal Data Recycling for Real-Time 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/2512.08557/) (2025)
- [Accelerated Rotation-Invariant Convolution for UAV Image Segmentation](https://mlsanity.com/paper/2512.08888/) (2025)

## Abstract (arXiv)

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that rely on pre-planned trajectories, constant %{satellite and network connectivity and reliable Global Navigation Satellite System (GNSS) positioning are brittle to failure. Drone embedded sense and detect offers a comprehensive mode of separation between drones for deconfliction and collision avoidance. This paper presents the first airborne LiDAR based solution for drone-swarm detection and localization using 3D deep learning model. It adapts an existing deep learning neural network to the air-to-air drone scenario by expanding the scan space vertically. A new sparse convolution is proposed and applied to accelerate the backbone layer, which is the most time-consuming part of the neural network. To collect training data of safety critical, close-proximity multi-drone operations, a scenario Digital Twin is used to augment real datasets with high fidelity synthetic data. The trained model achieves over 80% recall and 96% precision when tested on real-world datasets. By incorporating a tracking-by-detection algorithm the system can reliably monitor the separation distance of multiple drones in challenging environments.