# FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection

arXiv: https://arxiv.org/abs/2504.20670 · HTML: https://mlsanity.com/paper/2504.20670/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [FCOS: A simple and strong anchor-free object detector](https://mlsanity.com/paper/2006.09214/) (2020)
- [Clustered Object Detection in Aerial Images](https://mlsanity.com/paper/1904.08008/) (2019)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)
- [Perceptual Generative Adversarial Networks for Small Object Detection](https://mlsanity.com/paper/1706.05274/) (2017)
- [Augmentation for small object detection](https://mlsanity.com/paper/1902.07296/) (2019)
- [Density Map Guided Object Detection in Aerial Images](https://mlsanity.com/paper/2004.05520/) (2020)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [Scale-aware Fast R-CNN for Pedestrian Detection](https://mlsanity.com/paper/1510.08160/) (2015)
- [SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications](https://mlsanity.com/paper/1907.11093/) (2019)
- [IPG-Net: Image Pyramid Guidance Network for Small Object Detection](https://mlsanity.com/paper/1912.00632/) (2019)

## Built on by (most cited first, 13 of 13)

- [TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection](https://mlsanity.com/paper/2509.22909/) (2025)
- [PRNet: Original Information Is All You Have](https://mlsanity.com/paper/2510.09531/) (2025)
- [WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects](https://mlsanity.com/paper/2604.07759/) (2026)
- [CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling](https://mlsanity.com/paper/2607.05177/) (2026)
- [TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery](https://mlsanity.com/paper/2509.06035/) (2025)
- [SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks](https://mlsanity.com/paper/2509.17086/) (2025)
- [An Empirical Study on the Robustness of YOLO Models for Underwater Object Detection](https://mlsanity.com/paper/2509.17561/) (2025)
- [FMC-DETR: Frequency-Decoupled Multi-Domain Coordination for Aerial-View Object Detection](https://mlsanity.com/paper/2509.23056/) (2025)
- [Cross-Layer Feature Self-Attention Module for Multi-Scale Object Detection](https://mlsanity.com/paper/2510.14726/) (2025)
- [VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection](https://mlsanity.com/paper/2605.10229/) (2026)
- [LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection](https://mlsanity.com/paper/2605.20667/) (2026)
- [From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection](https://mlsanity.com/paper/2606.23825/) (2026)
- [Cotton-SF YOLO: Learning Structural and Frequency Cues for Early Cotton Square Detection in Complex Field Environments](https://mlsanity.com/paper/2607.14445/) (2026)

## Abstract (arXiv)

Embedded flight devices with visual capabilities have become essential for a wide range of applications. In aerial image detection, while many existing methods have partially addressed the issue of small target detection, challenges remain in optimizing small target detection and balancing detection accuracy with efficiency. These issues are key obstacles to the advancement of real-time aerial image detection. In this paper, we propose a new family of real-time detectors for aerial image detection, named FBRT-YOLO, to address the imbalance between detection accuracy and efficiency. Our method comprises two lightweight modules: Feature Complementary Mapping Module (FCM) and Multi-Kernel Perception Unit(MKP), designed to enhance object perception for small targets in aerial images. FCM focuses on alleviating the problem of information imbalance caused by the loss of small target information in deep networks. It aims to integrate spatial positional information of targets more deeply into the network,better aligning with semantic information in the deeper layers to improve the localization of small targets. We introduce MKP, which leverages convolutions with kernels of different sizes to enhance the relationships between targets of various scales and improve the perception of targets at different scales. Extensive experimental results on three major aerial image datasets, including Visdrone, UAVDT, and AI-TOD,demonstrate that FBRT-YOLO outperforms various real-time detectors in terms of performance and speed.