# Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet

arXiv: https://arxiv.org/abs/2311.03221 · HTML: https://mlsanity.com/paper/2311.03221/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1710.07563/) (2017)
- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Machine Learning-Assisted UAV Operations with UTM: Requirements, Challenges, and Solutions](https://mlsanity.com/paper/2006.14544/) (2020)

## Abstract (arXiv)

The integration of unmanned aerial vehicles (UAVs) into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defense. A critical prerequisite for this integration is equipping UAVs with enhanced situational awareness to ensure safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for UAVs. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in UAVs, facilitating safe and efficient BVLOS operations.