用YOLOv5在航拍图像中实时识别救援关键车辆与事故
YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery
- 基于自建数据集，全流程训练并优化YOLOv5检测模型
- 在复杂背景和小目标场景下保持高精度与实时性
- 适合应急响应系统开发人员参考，提升灾情感知效率
本文提出一种基于YOLOv5的航拍图像目标检测方法，聚焦于识别救护车、车祸现场、警车、拖车、消防车、侧翻车辆及着火车辆等关键对象。通过构建自定义数据集，完整呈现从数据采集、标注到模型训练与评估的流程。实验表明，YOLOv5在复杂背景和小目标检测任务中表现出良好的速度与准确率平衡，适用于实时应急响应系统。该研究为自动化灾情感知提供了有效方案，并指明了未来发展方向。
原文摘要 · Abstract (English)
This paper presents a robust approach for object detection in aerial imagery using the YOLOv5 model. We focus on identifying critical objects such as ambulances, car crashes, police vehicles, tow trucks, fire engines, overturned cars, and vehicles on fire. By leveraging a custom dataset, we outline the complete pipeline from data collection and annotation to model training and evaluation. Our results demonstrate that YOLOv5 effectively balances speed and accuracy, making it suitable for real-time emergency response applications. This work addresses key challenges in aerial imagery, including small object detection and complex backgrounds, and provides insights for future research in automated emergency response systems.
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