Abstract
Foreign Object Debris (FOD) within aircraft fuel tanks presents critical safety hazards including fuel contamination, system malfunctions, and increased maintenance costs. Despite the severity of these risks, there is a notable lack of dedicated datasets for the complex, enclosed environments found inside fuel tanks. To bridge this gap, we present a novel dataset, FOD-S2R, composed of real and synthetic images of the FOD within a simulated aircraft fuel tank. Unlike existing datasets that focus on external or open-air environments, our dataset is the first to systematically evaluate the effectiveness of synthetic data in enhancing the real-world FOD detection performance in confined, closed structures. The real-world subset consists of 3,114 high-resolution HD images captured in a controlled fuel tank replica, while the synthetic subset includes 3,137 images generated using Unreal Engine. The dataset is composed of various Field of views (FOV), object distances, lighting conditions, color, and object size. Prior research has demonstrated that synthetic data can reduce reliance on extensive real-world annotations and improve the generalizability of vision models. Thus, we benchmark several state-of-the-art object detection models and demonstrate that introducing synthetic data improves the detection accuracy and generalization to real-world conditions. These experiments demonstrate the effectiveness of synthetic data in enhancing the model performance and narrowing the Sim2Real gap, providing a valuable foundation for developing automated FOD detection systems for aviation maintenance.
Abstract (translated)
飞机燃油箱内的外来物（Foreign Object Debris，FOD）会带来重大的安全风险，包括燃料污染、系统故障以及增加的维护成本。尽管这些风险非常严重，但专门针对封闭环境中复杂环境的数据集仍然缺乏。为解决这一问题，我们提出了一项全新的数据集——FOD-S2R，该数据集由真实和合成图像组成，展示了模拟飞机燃油箱内部的FOD情况。与现有的专注于外部或开阔环境的数据集不同，我们的数据集首次系统地评估了合成数据在增强封闭空间中实际FOD检测性能方面的有效性。 具体来说，现实世界的子集包含3,114张高分辨率HD图像，这些图像是在一个受控的燃油箱复制品环境中拍摄得到的。而合成数据子集则包括使用Unreal Engine生成的3,137张图像。该数据集涵盖了各种视场（Field of View）、物体距离、光照条件、颜色和物体尺寸。 先前的研究表明，合成数据可以减少对大量实际世界标注的依赖，并提高视觉模型的泛化能力。因此，我们对几种最先进的目标检测模型进行了基准测试，并展示了引入合成数据能够提升检测准确性和在真实环境中的泛化性能。这些实验证明了合成数据在增强模型性能和缩小仿真到现实（Sim2Real）差距方面是有效的，为开发用于航空维护的自动化FOD检测系统提供了宝贵的基石。
URL
https://arxiv.org/abs/2512.01315
