# Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments

arXiv: https://arxiv.org/abs/2504.20097 · HTML: https://mlsanity.com/paper/2504.20097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 65 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [A Review of Deep Learning Techniques for Speech Processing](https://mlsanity.com/paper/2305.00359/) (2023)
- [Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers](https://mlsanity.com/paper/1905.06700/) (2019)
- [Single-photon imaging over 200 km](https://mlsanity.com/paper/2103.05860/) (2021)
- [Neural network identification of people hidden from view with a single-pixel, single-photon detector](https://mlsanity.com/paper/1709.07244/) (2017)
- [Single-photon computational 3D imaging at 45 km](https://mlsanity.com/paper/1904.10341/) (2019)
- [Spatial images from temporal data](https://mlsanity.com/paper/1912.01413/) (2019)
- [High-speed object detection with a single-photon time-of-flight image sensor](https://mlsanity.com/paper/2107.13407/) (2021)
- [Long-range depth imaging using a single-photon detector array and non-local data fusion](https://mlsanity.com/paper/1812.04943/) (2018)
- [3D imaging from multipath temporal echoes](https://mlsanity.com/paper/2011.09284/) (2020)
- [Single-pixel imaging based on deep learning](https://mlsanity.com/paper/2310.16869/) (2023)
- [Single-pixel 3D imaging based on fusion temporal data of single photon detector and millimeter-wave radar](https://mlsanity.com/paper/2312.12439/) (2023)

## Abstract (arXiv)

Detecting small objects, such as drones, over long distances presents a significant challenge with broad implications for security, surveillance, environmental monitoring, and autonomous systems. Traditional imaging-based methods rely on high-resolution image acquisition, but are often constrained by range, power consumption, and cost. In contrast, data-driven single-photon-single-pixel light detection and ranging (textDtextsuperscript2SPtextsuperscript2-LiDAR) provides an imaging-free alternative, directly enabling target identification while reducing system complexity and cost. However, its detection range has been limited to a few hundred meters. Here, we introduce a novel integration of residual neural networks (ResNet) with textDtextsuperscript2SPtextsuperscript2-LiDAR, incorporating a refined observation model to extend the detection range to 5~sikilometer in an intracity environment while enabling high-accuracy identification of drone poses and types. Experimental results demonstrate that our approach not only outperforms conventional imaging-based recognition systems, but also achieves 94.93% pose identification accuracy and 97.99% type classification accuracy, even under weak signal conditions with long distances and low signal-to-noise ratios (SNRs). These findings highlight the potential of imaging-free methods for robust long-range detection of small targets in real-world scenarios.