NSF Award
2318546
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.<br/><br/>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.<br/><br/>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.
