S Priyadharsini, K Bhuvaneshwara Raja, T Kousi Krishnan, Jagatheesaperumal Senthil Kumar, Alkhamees Bader Fahad, Hassan Mohammad Mehedi
Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India.
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India.
PeerJ Comput Sci. 2025 Jan 21;11:e2570. doi: 10.7717/peerj-cs.2570. eCollection 2025.
Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install.
This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. The classification module categorizes FOD into specific types of foreign objects. In the object localization module, these classified objects are pinpointed in video frames.
The proposed system was experimentally tested with a large video dataset and compared with existing methods. The results demonstrated improved accuracy and robustness, allowing the FOD clearance team to quickly detect and remove foreign objects, thereby enhancing the safety and efficiency of airport runway operations.
外来物碎片（FOD）是一种会损坏车辆系统的有害物质，最常见的是损坏车辆的车轮。在机场跑道上，这些外来物会损坏飞机的车轮或内部系统，有可能导致飞行事故。调查表明，与外来物碎片相关的损失每年超过40亿美元，影响到航空公司、机场租户和乘客。当前清除外来物碎片的工作涉及高成本的雷达和大量人力，而且现有的基于雷达和摄像头的监测方法安装成本高昂。
这项工作提出了一种基于视频的深度学习方法，以解决基于雷达的外来物碎片检测成本高昂的问题。所提出的系统由两个用于外来物碎片检测的模块组成：目标分类和目标定位。分类模块将外来物碎片归类为特定类型的外来物体。在目标定位模块中，这些已分类的物体在视频帧中被精确找出。
所提出的系统使用一个大型视频数据集进行了实验测试，并与现有方法进行了比较。结果表明其准确性和鲁棒性有所提高，使外来物碎片清除团队能够快速检测并清除外来物体，从而提高了机场跑道运营的安全性和效率。
