SBIR Phase I: A SMART CAMERA SYSTEM FOR WATER RISK ASSESSMENT

The broader impact of this SBIR Phase I project is to reduce the loss of human life and property by providing improved current and future information to water management stakeholders. Rising sea levels and heavier downpours could increase flooding costs in coastal communities by $23 billion per year by mid-century. Traditional water monitoring sensors have major drawbacks, such as: constant maintenance, costly installation, and application-specific dedicated hardware. Continuous monitoring of flood prone areas and optimized water management for freshwater conservation requires new technologies, including low-cost and robust water control systems to limit the loss of human lives, crops, property, and livestock. The proposed system in this project will offer a robust solution to monitor water and make weather predictions using deep learning algorithms. This SBIR project proposes to develop a cost-effective smart camera-based solution to perform a highly accurate risk assessment of flooding in public waterways.A key innovation is running sophisticated computer vision algorithms on a resource-limited platform. The proposed system will use low-cost LIDARs, generally used for measuring solid object velocities, for water velocity measurements. Furthermore, the project will develop a polarized-light system to achieve the system accuracy requirements, including real-time corrections of errors induced by visual impairments of the camera due to field conditions. 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.