从无人机检查视频中实时对象检测,通过将YOLOv5s和DeepStream结合起来
Shidun Xie1, Guanghong Deng1, Baihao Lin1
1Guangdong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究介绍了一种人工智能驱动的系统,用于高空无人机 (UAV) 检查,提高了对象检测的准确性和速度. 轻量级的YOLOv5s模型与增强的DeepStream框架集成,可实现高效的实时自动化检查.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 使用无人飞行器 (UAV) 的高空检查面临诸多挑战,包括天气干扰,信号干扰和物体可见度降低.
- 现有的方法在复杂的空中环境中难以处理实时数据和可靠的对象识别.
研究的目的:
- 开发一个自动化的无人机检查系统,利用人工智能来增强对象检测.
- 创建一个轻量级和高效的AI模型,适合在无人机上进行边缘部署.
- 通过框架修改,提高检查系统的稳定性和可用性.
主要方法:
- 使用了无人机系统调度平台和人工智能对象检测的组合.
- 通过对各种车辆数据集进行训练,YOLOv5s模型实现了高精度指标 (mAP50:93.2%,mAP50-95:71.7%).
- DeepStream框架被修改为HTTP通信,异步报警功能,并改进了视频流恢复以实现实时部署.
主要成果:
- YOLOv5s模型展示了小文件大小 (13.76 MB) 和快速检测速度 (每张图像11.26 ms),非常适合边缘计算.
- 集成系统成功地进行了自动无人机检查,提高了效率和可靠性.
- 框架的改进促进了用户同时访问和弹性视频流管理.
结论:
- 开发的人工智能驱动的无人机检查系统为实时空中监控提供了一个轻量级,高效和强大的解决方案.
- 优化的YOLOv5s模型和增强的DeepStream框架为在具有挑战性的条件下进行自动检查提供了一个可扩展的平台.
- 这种方法显著提高了无人机在高空监视和数据收集方面的能力.
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