论文标题
VERIFIRE：验证一种基于学习的，基于学习的野火检测系统
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
论文作者
论文摘要
在这篇简短的论文中，我们介绍了在Verifire项目上正在进行的工作 - 行业与学术界之间的合作，旨在使用验证来提高现实世界中，安全至关重要的系统的可靠性。我们靶向的系统是一个用于野火检测的机载平台，它结合了两个深神经网络。我们描述了感兴趣的系统及其属性，并讨论了我们试图验证系统一致性的尝试，即，即使它描述了强度的增加，即使其继续并正确地对给定输入进行了正确分类的能力。我们认为这项工作是将面向学术的验证工具纳入现实世界中感兴趣的系统的一步。
In this short paper, we present our ongoing work on the veriFIRE project -- a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system. The system we target is an airborne platform for wildfire detection, which incorporates two deep neural networks. We describe the system and its properties of interest, and discuss our attempts to verify the system's consistency, i.e., its ability to continue and correctly classify a given input, even if the wildfire it describes increases in intensity. We regard this work as a step towards the incorporation of academic-oriented verification tools into real-world systems of interest.
