# YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion

arXiv: https://arxiv.org/abs/2503.07115 · HTML: https://mlsanity.com/paper/2503.07115/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [TDN: Temporal Difference Networks for Efficient Action Recognition](https://mlsanity.com/paper/2012.10071/) (2020)
- [Memory Enhanced Global-Local Aggregation for Video Object Detection](https://mlsanity.com/paper/2003.12063/) (2020)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery](https://mlsanity.com/paper/2209.13351/) (2022)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning](https://mlsanity.com/paper/2308.09534/) (2023)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [Dogfight: Detecting Drones from Drones Videos](https://mlsanity.com/paper/2103.17242/) (2021)
- [Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning](https://mlsanity.com/paper/2308.04828/) (2023)
- [Flying Objects Detection from a Single Moving Camera](https://mlsanity.com/paper/1411.7715/) (2014)
- [TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos](https://mlsanity.com/paper/2210.08423/) (2022)
- [Visible and Clear: Finding Tiny Objects in Difference Map](https://mlsanity.com/paper/2405.11276/) (2024)
- [Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion](https://mlsanity.com/paper/2312.11008/) (2023)
- [A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion](https://mlsanity.com/paper/2109.01800/) (2021)
- [XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos](https://mlsanity.com/paper/2407.18137/) (2024)
- [Motion-guided small MAV detection in complex and non-planar scenes](https://mlsanity.com/paper/2410.10527/) (2024)

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

- [Edge-Aware Thermal Infrared UAV Swarm Tracking](https://mlsanity.com/paper/2607.12544/) (2026)
- [Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance](https://mlsanity.com/paper/2610.08315/) (2026)

## Abstract (arXiv)

Vision-based drone-to-drone detection has attracted increasing attention due to its importance in numerous tasks such as vision-based swarming, aerial see-and-avoid, and malicious drone detection. However, existing methods often encounter failures when the background is complex or the target is tiny. This paper proposes a novel end-to-end framework that accurately identifies small drones in complex environments using motion guidance. It starts by creating a motion difference map to capture the motion characteristics of tiny drones. Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone. Finally, the fused feature map is processed through an enhanced backbone and detection head based on the YOLOv5 framework to achieve accurate detection results. To validate our method, we propose a new dataset, named ARD100, which comprises 100 videos (202,467 frames) covering various challenging conditions and has the smallest average object size compared with the existing drone detection datasets. Extensive experiments on the ARD100 and NPS-Drones datasets show that our proposed detector performs exceptionally well under challenging conditions and surpasses state-of-the-art algorithms across various metrics. We publicly release the codes and ARD100 dataset at https://github.com/Irisky123/YOLOMG.