用无人机视频提升交通中车辆检测，融合时空信息效果更好
Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring
- 基于YOLO改进时空模型，利用连续帧动态信息提升检测
- 最佳模型比单帧模型提升16.22%检测准确率
- 注意力机制可进一步优化性能，适合交通监控场景
本研究通过开发时空目标检测模型，提升无人机摄像头在交通监控中的多类别车辆检测能力。提出了一个包含6,600张标注序列图像的无人机时空车辆检测数据集（STVD），支持算法的全面训练与评估。基于YOLO的检测算法被改进以融入时间动态信息，显著优于单帧模型。实验表明，最优时空模型相比单帧模型提升16.22%；同时，注意力机制的引入展现出进一步提升性能的潜力。
原文摘要 · Abstract (English)
This work presents advancements in multi-class vehicle detection using UAV cameras through the development of spatiotemporal object detection models. The study introduces a Spatio-Temporal Vehicle Detection Dataset (STVD) containing 6, 600 annotated sequential frame images captured by UAVs, enabling comprehensive training and evaluation of algorithms for holistic spatiotemporal perception. A YOLO-based object detection algorithm is enhanced to incorporate temporal dynamics, resulting in improved performance over single frame models. The integration of attention mechanisms into spatiotemporal models is shown to further enhance performance. Experimental validation demonstrates significant progress, with the best spatiotemporal model exhibiting a 16.22% improvement over single frame models, while it is demonstrated that attention mechanisms hold the potential for additional performance gains.
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