CISE-MSI:DP:Real-Time Aerial Imaging with Edge AI

该项目旨在开发一种创新的航空成像系统，该系统结合了最先进的人工智能(AI)，用于实时数据处理和分析。这家合资企业是诺福克州立大学计算机科学和工程系的学生和教职员工之间的合作，他们将设计、开发和测试一个功能齐全的系统，该系统能够执行诸如自动和远程控制导航、图像和自动驾驶等任务。该项目的重要意义在于它有可能给航空成像和数据分析领域带来革命性的变化，其应用范围扩展到农业、环境监测、灾害应对等领域。通过将EDGE AI算法和软件模块集成到系统中，该团队将实现精确的数据处理和增强的决策能力。此外，该项目致力于通过为代表性不足的少数族裔社区和妇女提供机会，提高工程领域的多样性和代表性。该项目旨在通过将双摄像机视觉和飞行时间技术整合到无人驾驶飞行器中来开发航空机器视觉。这些技术的融合将产生数据丰富的多光谱模型，从而能够为作物监测、产量评估和杂草识别等应用创建大比例尺地图。该项目还旨在构建无人驾驶飞行器的协作系统，优化飞行参数和相机分辨率，以便从航空图像中准确地重建三维。此外，该项目将促进图像处理和决策支持的边缘智能，使用深度学习从高光谱模型的特定部分提取信息。这将允许快速而准确的决策，应用程序包括来自航空视频的树结构和树叶特征识别。该项目还将探索在多核中央处理器上实施复杂算法以提高性能。通过这个项目，该团队将为学生提供对无人机挑战和机遇的真实世界了解，以及它们如何与计算机视觉、机器学习和通信协议等技术相结合。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to develop an innovative aerial imaging system that incorporates state-of-the-art artificial intelligence (AI) for real-time data processing and analysis. This venture is a collaboration between students and faculty of the Computer Science and Engineering departments at Norfolk State University who will design, develop, and test a fully functional system capable of executing tasks such as autonomous and remote-controlled navigation, imagery, and autopilot. The significance of the project is embedded in its potential to revolutionize the field of aerial imaging and data analysis, with applications extending to agriculture, environmental monitoring, disaster response, and more. By integrating edge AI algorithms and software modules into the system, the team is set to achieve precise data processing and enhanced decision-making capabilities. Additionally, the project aspires to enhance diversity and representation in the field of engineering by providing opportunities for underrepresented minority communities and women.This project is designed to develop aerial machine vision by integrating dual-camera vision and time-of-flight technology into unmanned aerial vehicles. The amalgamation of these technologies will generate data-rich multispectral models, enabling the creation of large-scale maps for applications such as crop monitoring, yield assessment, and weed identification. The project also aims to construct collaborative systems of unmanned aerial vehicles, optimizing flight parameters and camera resolution for accurate three-dimensional reconstruction from aerial images. Furthermore, the project will facilitate edge intelligence for image processing and decision support, employing deep learning to extract information from specific segments of a hyperspectral model. This will allow for rapid and precise decision making, with applications including tree-structure and leaf-feature recognition from aerial videos. The project will also explore the implementation of complex algorithms on multicore central processing units for enhanced performance. Through this project, the team will provide students with a real-world understanding of the challenges and opportunities of unmanned aerial vehicles and how they integrate with technologies such as computer vision, machine learning, and communication protocols.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.