# Robust Airport Surface Object Detection Based on Graph Neural Network
###### Abstract  
Accurate and robust object detection is of critical importance in airport surface surveillance to ensure the security of air transportation systems. Owing to the constraints imposed by a relatively fixed receptive field, existing airport surface detection methods have not yet achieved substantial advancements in accuracy. Furthermore, these methods are vulnerable to adversarial attacks with carefully crafted adversarial inputs. To address these challenges, we propose the Vision GNN-Edge (ViGE) block, an enhanced block derived from the Vision GNN (ViG). ViGE introduces the receptive field in pixel space and represents the spatial relation between pixels directly. Moreover, we implement an adversarial training strategy with augmented training samples generated by adversarial perturbation. Empirical evaluations on the public remote sensing dataset LEVIR and a manually collected airport surface dataset show that: 1. our proposed method surpasses the original model in precision and robustness; 2. defining the receptive field in pixel space performs better than that on representation space. a airport surface; object detection; graph neural network; adversarial training +
Footnote †: journal: Medical  
neighborhood is designed to contain all patches, resulting in a receptive field covering the entire feature map. Though they can reach great performance, these methods suffer from substantial computational costs. Apart from these two methods, object detection based on graph neural networks (GNN) has been proposed nowadays. GNN is traditionally used for non-Euclidean data with inherent graphical structures, such as a molecular graph with its atoms and chemical bonds corresponding to nodes and edges, respectively. GNN was first introduced in [12; 13], and the graph convolutional network [14] was proposed based on the spectral graph theory [15]. In recent years, a series of GNNs were introduced to deal with graph data efficiently, such as mixture model network [16], message passing neural network [17], and graph attention network [18]. The usages of GNN for computer vision mainly involve scene graph construction [19] and point cloud classification [20] because these two issues contain naturally constructed graphs. When GNN is introduced to image feature extraction, the first and most important problem is how to construct a graph for an image or a feature map. For a pixel in the feature map, Vision GNN (ViG) [21] defines its neighborhood (receptive field) as k-nearest-neighbor in representation space, which achieves great performance. However, as there is not a naturally constructed graph structure among all pixels, it is of great significance to explore which is a better way to construct a graph on pixels when introducing GNN into image feature extraction. Recently, many researchers began to study how to utilize object detection in airport scenes. For airport surface object detection, [PERSON] et al. [1] proposed an attentional multiscale feature fusion enhancement network based on CNN. [PERSON] et al. [22] applied Ting-YOLO to airport aircraft object recognition and proposed a simplified Ting-YOLO framework to improve the detection speed. [PERSON] et al. applied the YOLO v3 [23] algorithm to the field of aircraft detection on the airport surface and proposed several improvements. [PERSON] [24] introduced a unified and effective moving object detection architecture, which combined appearance and motion cues. As far as we know, there is no research exploring GNN-based method potential in this domain. For airport surface object detection, the adversarial attack is considered a big challenge for model robustness and has been studied by more and more researchers. Adversarial attacks, whether arbitrarily caused or artificially crafted, are mainly performed by adding perturbation to the input image. Arbitrary perturbation results from weather changes, transmission failure, camera failure, and so on. This kind of perturbation can be detected by human eyes and solved immediately.