# TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos

arXiv: https://arxiv.org/abs/2210.08423 · HTML: https://mlsanity.com/paper/2210.08423/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 47 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [End-to-End Video Instance Segmentation with Transformers](https://mlsanity.com/paper/2011.14503/) (2020)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Video Transformer Network](https://mlsanity.com/paper/2102.00719/) (2021)
- [TCLR: Temporal Contrastive Learning for Video Representation](https://mlsanity.com/paper/2101.07974/) (2021)
- [Memory Enhanced Global-Local Aggregation for Video Object Detection](https://mlsanity.com/paper/2003.12063/) (2020)
- [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021)
- [Sequence Level Semantics Aggregation for Video Object Detection](https://mlsanity.com/paper/1907.06390/) (2019)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [Self-supervised Video Transformer](https://mlsanity.com/paper/2112.01514/) (2021)
- [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022)
- [SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking](https://mlsanity.com/paper/2106.08816/) (2021)
- [Dogfight: Detecting Drones from Drones Videos](https://mlsanity.com/paper/2103.17242/) (2021)
- [Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos](https://mlsanity.com/paper/2004.11475/) (2020)
- [Human Action Recognition in Drone Videos using a Few Aerial Training Examples](https://mlsanity.com/paper/1910.10027/) (2019)
- [Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks](https://mlsanity.com/paper/2105.12797/) (2021)
- [Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey](https://mlsanity.com/paper/2105.01347/) (2021)

## Built on by (most cited first, 10 of 10)

- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network](https://mlsanity.com/paper/2211.16785/) (2022)
- [C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks](https://mlsanity.com/paper/2404.19276/) (2024)
- [XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos](https://mlsanity.com/paper/2407.18137/) (2024)
- [Real-Time Detection for Small UAVs: Combining YOLO and Multi-frame Motion Analysis](https://mlsanity.com/paper/2411.02582/) (2024)
- [Motion-guided small MAV detection in complex and non-planar scenes](https://mlsanity.com/paper/2410.10527/) (2024)
- [YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion](https://mlsanity.com/paper/2503.07115/) (2025)
- [Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data](https://mlsanity.com/paper/2411.09077/) (2024)
- [ReynoldsFlow: Physics-Inspired Spatiotemporal Flow Representation for Video Understanding](https://mlsanity.com/paper/2503.04500/) (2025)
- [Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVs](https://mlsanity.com/paper/2510.16624/) (2025)

## Abstract (arXiv)

Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. However, 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. In this work, we propose a simple yet effective framework, textitTransVisDrone, that provides an end-to-end solution with higher computational efficiency. We utilize CSPDarkNet-53 network to learn object-related spatial features and VideoSwin model to improve drone detection in challenging scenarios by learning spatio-temporal dependencies of drone motion. Our method achieves state-of-the-art performance on three challenging real-world datasets (Average Precision@0.5IOU): NPS 0.95, FLDrones 0.75, and AOT 0.80, and a higher throughput than previous methods. We also demonstrate its deployment capability on edge devices and its usefulness in detecting drone-collision (encounter). Project: urlhttps://tusharsangam.github.io/TransVisDrone-project-page/.