Towards optimal foreign object debris detection in an airport environment
Towards optimal foreign object debris detection in an airport environment
复制标题
实现机场环境中的最佳异物碎片检测
DOI：
10.1016/j.eswa.2022.118829
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ankita Shah
中科院分区：
文献类型：
--
作者：
Mohammad Noroozi;Ankita Shah
异物碎片 (FOD) 会严重损坏飞机发动机并导致机场环境中的人员受伤。机场检查员使用传统和自动化方法定期检查跑道是否存在 FOD 物品，这些物品的材料、形状和颜色各不相同。当前方法的主要缺点是无法准确及时地检测所有类型的异物并将其从机场跑道上清除。在本研究中，我们通过开发一个物体检测框架来检测 FOD 以便快速从机场清除，从而解决了检测缺乏准确性和及时性的缺点。我们提出的 FOD 检测框架包括（i）用于检查和收集机场数据的无人机系统（UAS），（ii）数据预处理和增强技术，以解决对数据集中存在的有限类型的异物、天气条件和机场表面材料的学习问题，以及（iii）基于计算机视觉的物体检测模型，以实现在现实机场环境中部署的高精度和更快的推理时间。我们在空军靶场使用 UAS 生成训练数据，并开发了各种模型，包括我们框架中的 You Only Look Once (YOLO) 物体检测器系列模型（单级物体检测器）。我们的模型根据 UAS 收集的以前未见过的数据和公开的 FOD 图像数据集进行评估。实验结果表明，我们提出的采用具有迁移学习功能的 YOLO 模型版本 (YOLOv4) 的方法为 FOD 检测提供了更快的推理时间，并且在精度和召回率指标值方面优于其他模型。
Foreign object debris (FOD) can critically damage aircraft engines as well as injure personnel in an airport environment. Airfield inspectors routinely inspect runways for the presence of FOD items, which differ in material, shape, and color, using conventional and automated methods. The major shortcoming of the current methods is their inability to detect all types of foreign objects in an accurate and timely manner for removal from the airport runways. In this study, we address this shortcoming, i.e., the lack of accuracy and timeliness in detection, by developing an object detection framework to detect FOD for quick removal from the airfields. Our proposed FOD detection framework consists of (i) unmanned aerial system (UAS) for inspecting and collecting data from the airfields, (ii) data preprocessing and augmentation techniques to counter the issue of learning on limited types of foreign objects, weather conditions, and airport surface materials that are present in the data sets, and (iii) a computer vision-based object detection model to attain high accuracy and faster inference time for deployment in a real-world airport environment. We generated the training data with the UAS at an air force range and developed various models, including the You Only Look Once (YOLO) object detector family of models (one-stage object detectors) in our framework. Our models are evaluated on previously unseen data collected by the UAS and a publicly available data set of FOD images. The experiment results demonstrate that our proposed approach with a version of the YOLO model (YOLOv4) with transfer learning provides a faster inference time for FOD detection and outperforms the other models in the precision and recall metric values.
影响因子：
8.1
作者：
Shorten, Connor;Khoshgoftaar, Taghi M.
通讯作者：
Khoshgoftaar, Taghi M.