Multiscale Contextual Fusion for Robust Airport Surveillance

Object detection in airport surface surveillance presents significant challenges, primarily due to the extreme variation in object scales and the critical need for contextual information. To address these issues, we propose a novel deep learning architecture that integrates two specialized modules: the Poly Kernel Inception (PKI) module and the Context Anchor Attention (CAA) module. The PKI module is designed to effectively capture multi-scale features, enabling the accurate detection of objects ranging from large aircraft to small staff members. Concurrently, the CAA module leverages long-range contextual information, which significantly enhances the model’s ability to precisely localize and identify targets within complex scenes. The synergistic integration of these two modules demonstrates a substantial improvement in feature extraction performance, leading to enhanced detection accuracy on our publicly available ASS dataset. This work provides a robust and effective solution for the challenging task of airport surface object detection, establishing a strong foundation for future research in this domain.