消防水喷射轨迹检测来自无人机图像使用可学习的提示向量
Hengyu Cheng1, Jinsong Zhu1,2,3, Sining Wang1
1School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221006, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究引入了一种使用无人机图像和离线可学习的提示向量模块 (OPVM) 进行精确的喷气轨迹监测的新方法. 该系统达到95.4%的精度,提高了工业和消防应用中的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 航空航天工程 航空航天工程
背景情况:
- 精确监测喷气式飞行轨迹对于工业过程和消防的安全和效率至关重要.
- 现有的方法经常与图像中的几何和光度扭曲作斗争.
- 动态轨迹分析需要强大且计算效率高的解决方案.
研究的目的:
- 开发一种创新的方法来使用无人机 (UAV) 图像监测喷气式飞行轨迹.
- 通过图像增强和新型深度学习模块,提高喷气式飞行轨迹监测的准确性和稳定性.
- 创建一个计算效率高,适应现实世界应用的系统.
主要方法:
- 图像增强技术用于解决无人机捕获的喷气轨迹图像中的几何和光度扭曲.
- 部署Faster R-CNN网络,用于视频流中对象检测和精确的喷气轨迹识别.
- 集成一个离线可学习的提示向量模块 (OPVM) 进行精细的轨迹预测,包括视觉和文本功能 (双模方法).
主要成果:
- 在监测喷气轨迹方面取得了95.4%的非凡精度.
- 在实时轨迹监控方面表现出高效率.
- 在OPVM的线下训练最大限度地减少了额外的内存和计算资源需求.
结论:
- 拟议的基于无人机的方法提供了一个强大的框架,用于动态轨迹监测.
- 在OPVM的双模式方法显著提高了监测的准确性和稳定性.
- 这项研究为先进的轨迹检测和跟踪奠定了基础,在消防和工业系统中具有潜在的应用.
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