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.
Keywords: foreign object debris; object detection; optical imaging sensors; random forest.