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

SBIR 第一阶段：用于水风险评估的智能相机系统

• 批准号: 1938101
• 负责人: Burak Ozer
• 金额: $ 20.98万
• 依托单位: Pekosoft LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938101&HistoricalAwards=false
• 关键词: SBIR; Phase; SMART; CAMERA; SYSTEM

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.

SBIR第一阶段项目的更广泛的影响是通过向水管理利益相关者提供更好的当前和未来信息来减少人类生命和财产的损失。到本世纪中叶，海平面上升和暴雨可能会使沿海社区的洪水成本每年增加230亿美元。传统的水监测传感器具有主要缺点，例如：持续维护，昂贵的安装和特定应用的专用硬件。 持续监测洪水易发地区和优化淡水保护水管理需要新技术，包括低成本和强大的水控制系统，以限制人类生命、农作物、财产和牲畜的损失。该项目中的拟议系统将提供一个强大的解决方案，用于监测水资源，并使用深度学习算法进行天气预测。该SBIR项目旨在开发一种基于智能摄像头的高性价比解决方案，对公共水道的洪水风险进行高度准确的评估。一项关键创新是在资源有限的平台上运行复杂的计算机视觉算法。拟议的系统将使用低成本的激光雷达，通常用于测量固体物体的速度，用于水的速度测量。此外，该项目还将开发一个偏振光系统，以达到系统精度要求，包括实时校正由于现场条件导致的相机视觉障碍引起的误差。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。