A New Dataset and Research Approach for Movable Target Detection in Airports

Movable target detection on airport surfaces is fundamental to intelligent airport surveillance, yet the field lacks efficient detection algorithms and dedicated benchmark datasets. To address this gap, this paper introduces AFTD, a foreground target detection dataset for three types of movable airport targets: airplanes, vehicles, and persons. Through autonomous data acquisition and web data collection, over 200000 images were obtained, from which 10050 images were selected based on diversity to form the AFTD dataset. It contains 26968 airplane instances, 24759 vehicle instances, and 5064 person instances. AFTD covers diverse real-world challenges and provides fine-grained annotations, including three-level scale and occlusion annotations, as well as eight-level viewpoint annotations. Based on AFTD, we conducted extensive comparative experiments and confirmed that foundational detection algorithms are not robust to real-world scenarios. Subsequently, we propose a scene-specific prior-based approach for airport detection, along with its preliminary implementation. Furthermore, we demonstrate the cross-scenario generalizability of this research approach. AFTD can be downloaded from http://www.agvs-caac.com/aftd/aftd.html, and NSAD from https://github.com/AFTD-NSAD/AFTD-NSAD.