SPIE Journal Paper | 13 December 2022
KEYWORDS: Object detection, Data modeling, Education and training, Performance modeling, Feature fusion, Head, RGB color model, Statistical modeling, Radar sensor technology, Feature extraction
Foreign object debris (FOD) on airport runways has always been a major problem in maintaining airport security. Currently, there are two main challenges in FOD detection based on computer vision. First, FOD is small and inconspicuous, which makes it difficult for the detector to find and categorize the object correctly. Second, airport pavements at night present a low-light situation, which makes FOD detection more difficult. To solve these problems, we propose an improved real-time detector bidirectional YOLO (Bi-YOLO), constructed by first adding a bidirectional PANet to YOLOv5. This adds a BiFPN-like weighted bidirectional operation to the PANet, allowing the network to improve the focus on small objects adaptively. Then, we use the anchor-free manner, SimOTA label assignment, and multibranch head to reduce the complexity of our model and improve the model’s performance in detecting small objects. Finally, we use special data augmentation strategies during training to improve the model’s performance on small objects in low-light situations. Experiments on the public dataset FOD-A and our own dataset FODInSues show that, compared with the popular object detectors YOLOv5, YOLOv3, CenterNet, and EfficientDet-D4, Bi-YOLO achieves the best performance in FOD detection, especially for small FOD in low-light situations.
