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。该项目由诺福克州立大学计算机科学与工程系的学生和教师合作，他们将设计、开发和测试一个功能齐全的系统，该系统能够执行自主和远程控制导航、图像和自动驾驶仪等任务。该项目的意义在于它有可能彻底改变航空成像和数据分析领域，并将其应用扩展到农业、环境监测、灾害响应等领域。通过将边缘人工智能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，该项目希望通过为代表性不足的少数民族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头视觉和飞行时间技术集成到无人机中来开发空中机器视觉。这些技术的融合将产生数据丰富的多光谱模型，从而为作物监测、产量评估和杂草识别等应用创建大规模地图。该项目还旨在构建无人机协同系统，优化飞行参数和相机分辨率，以便从航空图像中进行精确的三维重建。此外，该项目将促进边缘智能的图像处理和决策支持，采用深度学习从高光谱模型的特定部分提取信息。这将允许快速和精确的决策，应用包括从航拍视频中识别树木结构和树叶特征。该项目还将探索在多核中央处理单元上实现复杂算法以增强性能。通过这个项目，该团队将为学生提供一个真实世界的理解，了解无人机的挑战和机遇，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.