SBIR Phase I: AI Enabled Cameras for Fire Detection

SBIR 第一阶段：用于火灾检测的人工智能摄像机

• 批准号: 2014890
• 负责人: Emilio Pineda
• 金额: $ 22.48万
• 依托单位: VOLANT AERIAL INC
• 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: 2020-07-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014890&HistoricalAwards=false
• 关键词: SBIR; Phase; AI; Enabled; Cameras

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from the potential to reduce devastation from forest fires. Wildfires across the world increasingly cause devastating impacts on local economies, human life, air quality, natural resources, property value, jobs and wages. In 2018, more than 58 million wildfires burned 8.8 million acres in the U.S. A system that alerts authorities within minutes (vs. days) dramatically increases the ability to minimize property damage and loss of life. The goal of this project is to develop a precise computationally efficient AI-based software solution for thermal (infrared) imaging detection of fire, to be implemented on various remote and moving platforms, such as manned aircraft and unmanned aerial vehicles. The resulting technology will be more precise than current solutions due to the incorporation of a unique approach to machine learning based on the use of real-life and artificially created training datasets. In addition, it will be developed to function “at the edge” to enable local, real-time operations in remote locations. A Deep Convolutional Learning approach should dramatically improve current mathematical models, potentially saving thousands of lives and billions of dollars.This Small Business Innovation Research Phase I project will demonstrate how a solution utilizing Deep Convolutional Learning can be successfully applied to the detection of forest fires from moving or static platforms or in remote locations. The project takes a unique approach to image recognition using multiplication-free neural networks based only on the discriminator of a generative adversarial network (GAN) and training the model with real and artificially created datasets that address extensive variability of conditions. This will eliminate confusion in the detection of smoke plumes, which can be caused by clouds and reflections of bodies of water. Additionally, the development of low-power neural network algorithms for real-time processing of sensory data will enable the software to run on low-cost embedded computers for use in remote, resource-constrained environments. Expectations are for precision levels above 80%, false alarm rates at less than 1 per 24 hours, time to detection in minutes vs. days and ease of use in the most remote locations.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.

这一小型企业创新研究(SBIR)第一阶段项目的更广泛影响将产生于减少森林火灾破坏的潜力。世界各地的野火对当地经济、人类生活、空气质量、自然资源、财产价值、就业和工资造成越来越大的破坏性影响。2018年，超过5800万场野火烧毁了美国880万英亩土地。一个在几分钟(而不是几天)内向当局发出警报的系统极大地提高了将财产损失和生命损失降至最低的能力。该项目的目标是开发一种基于精确计算效率的基于人工智能的软件解决方案，用于火灾的热(红外)成像探测，将在各种远程和移动平台上实施，如有人驾驶飞机和无人驾驶飞行器。由于采用了一种基于真实生活和人工创建的训练数据集的独特机器学习方法，因此产生的技术将比目前的解决方案更精确。此外，它还将被开发为在“边缘”发挥作用，以便能够在远程地点进行本地实时操作。深度卷积学习方法将极大地改进当前的数学模型，潜在地拯救数千人的生命和数十亿美元。这个小企业创新研究第一阶段项目将演示如何将利用深度卷积学习的解决方案成功地应用于从移动或静态平台或偏远地点检测森林火灾。该项目采用了一种独特的方法来识别图像，只使用基于生成性对抗网络(GAN)的鉴别器的无乘法神经网络，并使用真实的和人工创建的数据集来训练模型，这些数据集解决了条件的广泛可变性。这将消除探测烟羽的混乱，因为烟羽可能是由云层和水体的反射引起的。此外，用于实时处理感官数据的低功率神经网络算法的开发将使该软件能够在低成本嵌入式计算机上运行，以便在远程、资源受限的环境中使用。期望达到80%以上的精度水平，每24小时误警率低于1，检测时间在几分钟内而不是几天内，以及在最偏远的地点易于使用。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。