# TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection

arXiv: https://arxiv.org/abs/2211.16317 · HTML: https://mlsanity.com/paper/2211.16317/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions](https://mlsanity.com/paper/1710.02382/) (2017)
- [Drone Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2107.01435/) (2021)

## Built on by (most cited first, 2 of 2)

- [SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network](https://mlsanity.com/paper/2211.16785/) (2022)
- [GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark](https://mlsanity.com/paper/2305.03425/) (2023)

## Abstract (arXiv)

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is required to that could efficiently tackle this problem. Infrared cameras are increasingly used for nighttime surveillance due to their wide applications in night vision equipment. This paper uses a deep learning-based TinyFeatureNet (TF-Net), which is an improved version of YOLOv5s, to accurately detect UAVs during the night using infrared (IR) images. In the proposed TF-Net, we introduce architectural changes in the neck and backbone of the YOLOv5s. We also simulated four different YOLOv5 models (s,m,n,l) and proposed TF-Net for a fair comparison. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s. TF-Net yielded the best results with 95.7% precision, 84% mAp, and 44.8% IoU.