Abstract
Aircraft detection is important for intelligent airport applications. This task is challenging due to some problems, e.g. the aircraft is usually small and the appearance varies dramatically with view angle. In this paper, we introduce the Automatic Dependent Surveillance-Broadcast (ADS-B) signal into the object detection framework. ADS-B is a kind of airport-specific data, which provides the aircraft location information in real-time. We use the ADS-B signal as prior information to guide aircraft detection. Firstly, from the spatial perspective, we construct an ADS-B-based saliency function, and use it to apply attention to certain spatial regions during feature extraction. Because the aircraft is likely to be in the area of attention, the detection accuracy can be improved, especially for small aircraft. Secondly, from the temporal perspective, we predict the motion direction of moving aircraft based on historical ADS-B data, and use it to generate real-time updated anchors. In addition, the shape and scale prior are also considered in the anchor generation process. The generated anchor is able to fit aircraft shape well, even in the case of drastic viewangle changes. Finally, experiments are conducted on the AGVS-T dataset to verify the effectiveness of the proposed method.
Supported by the National Natural Science Foundation of China under grants U1733111 and U19A2052, and partly by the Project of Quzhou Municipal Government (2022D034).
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Jiang, L., Zhang, X., Liu, Y., Li, T. (2023). ADS-B-Based Spatial-Temporal Multi-scale Object Detection Network for Airport Scenes. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14358. Springer, Cham. https://doi.org/10.1007/978-3-031-46314-3_27
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