基于 YOLOv5 的航空影像应急响应目标检测
YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery
AI总结：
本文构建自定义数据集并采用 YOLOv5 检测航空影像中的应急关键车辆与事故场景，验证其在速度和精度上的平衡及实时应急价值。
摘要：
本文提出了一种使用 YOLOv5 模型进行航空影像目标检测的鲁棒方法。我们重点识别救护车、车祸、警车、拖车、消防车、倾覆车辆以及起火车辆等关键目标。通过利用一个自定义数据集，我们概述了从数据采集与标注到模型训练和评估的完整流程。结果表明，YOLOv5 有效平衡了速度与精度，适用于实时应急响应应用。本工作解决了航空影像中的关键挑战，包括小目标检测和复杂背景，并为自动化应急响应系统的未来研究提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
