School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2022 Mar 23;22(7):2463. doi: 10.3390/s22072463.
In recent years, aviation security has become an important area of concern as foreign object debris (FOD) on the airport pavement has a huge potential risk to aircraft during takeoff and landing. Therefore, accurate detection of FOD is important to ensure aircraft flight safety. This paper proposes a novel method to detect FOD based on random forest. The complexity of information in airfield pavement images and the variability of FOD make FOD features difficult to design manually. To overcome this challenge, this study designs the pixel visual feature (PVF), in which weight and receptive field are determined through learning to obtain the optimal PVF. Then, the framework of random forest employing the optimal PVF to segment FOD is proposed. The effectiveness of the proposed method is demonstrated on the FOD dataset. The results show that compared with the original random forest and the deep learning method of Deeplabv3+, the proposed method is superior in precision and recall for FOD detection. This work aims to improve the accuracy of FOD detection and provide a reference for researchers interested in FOD detection in aviation.
近年来，航空安全成为一个重要关注点，因为机场跑道上的外来物碎片 (FOD) 在飞机起飞和降落时存在巨大的潜在风险。因此，准确检测 FOD 对于确保飞机飞行安全至关重要。本文提出了一种基于随机森林的 FOD 检测新方法。机场跑道图像中的信息复杂性和 FOD 的可变性使得 FOD 特征难以手动设计。为了克服这一挑战，本研究设计了像素视觉特征 (PVF)，通过学习确定权重和感受野，以获得最优的 PVF。然后，提出了一种采用最优 PVF 分割 FOD 的随机森林框架。在 FOD 数据集上验证了所提方法的有效性。结果表明，与原始随机森林和 Deeplabv3+ 的深度学习方法相比，所提方法在 FOD 检测的精度和召回率方面表现更优。本工作旨在提高 FOD 检测的准确性，为对航空 FOD 检测感兴趣的研究人员提供参考。
