# YOLO-Drone: An Efficient Object Detection Approach Using the GhostHead Network for Drone Images

arXiv: https://arxiv.org/abs/2511.10905 · HTML: https://mlsanity.com/paper/2511.10905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [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)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [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)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [Depthwise Convolution is All You Need for Learning Multiple Visual Domains](https://mlsanity.com/paper/1902.00927/) (2019)
- [Edge-guided Representation Learning for Underwater Object Detection](https://mlsanity.com/paper/2306.00440/) (2023)
- [Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery](https://mlsanity.com/paper/2312.15219/) (2023)
- [Can Urban Air Mobility become reality? Opportunities, challenges and selected research results](https://mlsanity.com/paper/2309.12680/) (2023)

## Built on by (most cited first, 1 of 1)

- [A text-guided vision model for enhanced recognition of small instances](https://mlsanity.com/paper/2602.19503/) (2026)

## Abstract (arXiv)

Object detection using images or videos captured by drones is a promising technology with significant potential across various industries. However, a major challenge is that drone images are typically taken from high altitudes, making object identification difficult. This paper proposes an effective solution to address this issue. The base model used in the experiments is YOLOv11, the latest object detection model, with a specific implementation based on YOLOv11n. The experimental data were sourced from the widely used and reliable VisDrone dataset, a standard benchmark in drone-based object detection. This paper introduces an enhancement to the Head network of the YOLOv11 algorithm, called the GhostHead Network. The model incorporating this improvement is named YOLO-Drone. Experimental results demonstrate that YOLO-Drone achieves significant improvements in key detection accuracy metrics, including Precision, Recall, F1-Score, and mAP (0.5), compared to the original YOLOv11. Specifically, the proposed model recorded a 0.4% increase in Precision, a 0.6% increase in Recall, a 0.5% increase in F1-Score, and a 0.5% increase in mAP (0.5). Additionally, the Inference Speed metric, which measures image processing speed, also showed a notable improvement. These results indicate that YOLO-Drone is a high-performance model with enhanced accuracy and speed compared to YOLOv11. To further validate its reliability, comparative experiments were conducted against other high-performance object detection models, including YOLOv8, YOLOv9, and YOLOv10. The results confirmed that the proposed model outperformed YOLOv8 by 0.1% in mAP (0.5) and surpassed YOLOv9 and YOLOv10 by 0.3% and 0.6%, respectively.