Historically, the rotorcraft community has experienced a higher fatal accident rate than other aviation segments, including commercial and general aviation. Recent advancements in artificial intelligence (AI) and the application of these technologies in different areas of our lives are both intriguing and encouraging. When developed appropriately for the aviation domain, AI techniques provide an opportunity to help design systems that can address rotorcraft safety challenges. Our recent work demonstrated that AI algorithms could use video data from onboard cameras and correctly identify different flight parameters from cockpit gauges, e.g., indicated airspeed. These AI-based techniques provide a potentially cost-effective solution, especially for small helicopter operators, to record the flight state information and perform post-flight analyses. We also showed that carefully designed and trained AI systems could accurately predict rotorcraft attitude (i.e., pitch and yaw) from outside scenes (images or video data). Ordinary off-the-shelf video cameras were installed inside the rotorcraft cockpit to record the outside scene, including the horizon. The AI algorithm could correctly identify rotorcraft attitude at an accuracy in the range of 80\%. In this work, we combined five different onboard camera viewpoints to improve attitude prediction accuracy to 94\%. In this paper, five onboard camera views included the pilot windshield, co-pilot windshield, pilot Electronic Flight Instrument System (EFIS) display, co-pilot EFIS display, and the attitude indicator gauge. Using video data from each camera view, we trained various convolutional neural networks (CNNs), which achieved prediction accuracy in the range of 79\% % to 90\% %. We subsequently ensembled the learned knowledge from all CNNs and achieved an ensembled accuracy of 93.3\%.

翻译：历史上，旋翼飞行器领域的事故死亡率一直高于包括商业航空和通用航空在内的其他航空领域。人工智能（AI）的近期进展及其在我们生活各领域的应用既令人着迷又鼓舞人心。当为航空领域适当开发时，AI技术提供了设计能够应对旋翼飞行器安全挑战的系统的机会。我们近期的工作表明，AI算法能够利用机载摄像头的视频数据，从驾驶舱仪表（例如指示空速）中正确识别不同的飞行参数。这些基于AI的技术为小型直升机运营商提供了一种潜在的低成本解决方案，用于记录飞行状态信息并进行飞行后分析。我们还展示了经过精心设计和训练的AI系统能够从外部场景（图像或视频数据）中准确预测旋翼飞行器的姿态（即俯仰和偏航）。普通的现成摄像机安装在旋翼飞行器驾驶舱内，用于记录包括地平线在内的外部场景。AI算法能以约80%的准确率正确识别旋翼飞行器姿态。在本研究中，我们结合了五个不同的机载摄像头视角，将姿态预测准确率提升至94%。这五个机载摄像头视角包括：飞行员挡风玻璃、副驾驶挡风玻璃、飞行员电子飞行仪表系统（EFIS）显示器、副驾驶EFIS显示器以及姿态指示仪表。利用每个摄像头视角的视频数据，我们训练了多种卷积神经网络（CNN），其预测准确率在79%至90%之间。随后，我们集成所有CNN的习得知识，实现了93.3%的集成准确率。