YOLO-Drone提升无人机图像目标检测精度与速度
YOLO-Drone: An Efficient Object Detection Approach Using the GhostHead Network for Drone Images
- 在YOLOv11基础上改进头部网络，引入轻量级GhostHead结构
- 在VisDrone数据集上实现mAP(0.5)提升0.5%，速度更快
- 特别适合高海拔无人机场景，优于YOLOv8-v10系列
基于无人机图像的目标检测技术在多个领域具有广泛应用前景，但因高空拍摄导致目标尺度小、识别困难。本文以最新的YOLOv11n为基础，提出YOLO-Drone模型，通过引入GhostHead网络优化检测头结构。实验基于标准无人机数据集VisDrone，结果表明：相较于原始YOLOv11，YOLO-Drone在Precision、Recall、F1-Score和mAP(0.5)上分别提升0.4%、0.6%、0.5%和0.5%；同时推理速度显著提升。与YOLOv8、YOLOv9、YOLOv10对比，mAP(0.5)分别高出0.1%、0.3%、0.6%，验证了其在精度与效率上的优越性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。