Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

中文翻译：

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基于无人机的主动红外热成像应用视频稳定技术的评估与选择

无人飞行器（UAV）可以在装有多个传感器（例如，热像仪和光学相机）的飞机周围飞行，以检查感兴趣的零件而又不将其卸下，这对减少检查时间和降低成本具有重大影响。基于无人机的主动红外热成像（IRT）检查中的主要挑战之一是无人机的意外动作。由于主动热成像技术主要与热序列分析有关，因此意外运动会干扰热轮廓分析，并导致数据误解，尤其是在提供此类检查的自动化过程管线时。此外，在打算由检查员进行后分析的情况下，无人机的意外动作可能会增加人为错误，数据误解以及对可能的缺陷进行不正确表征的风险。因此，需要使用数字视频稳定技术进行后处理以最小化/消除这种不希望的运动。有许多易于使用的视频稳定算法。然而，选择最合适的人也是一项挑战。因此，本文评估了视频稳定算法以最小化/减轻不希望的无人机运动，并提出了一种简单的方法来找到最合适的稳定算法，这是朝着全面运行的UAV-IRT检查系统迈出的第一步。