A New Benchmark Dataset and an Imprecise Supervised Learning Approach for Airport Semantic Segmentation

Airport semantic segmentation is fundamental to airport surveillance, yet there currently lacks a specialized benchmark and algorithms for this task. In this paper, we introduce ASS, the first large-scale Airport Semantic Segmentation dataset tailored for airport scenarios. ASS comprises 18 common semantic categories, 250 videos, and over 140,000 frames with precise manual annotations. The dataset covers a wide spectrum of challenges inherent to airport surveillance, including intra-class diversity, unusual shapes, inter-class similarity, extreme multi-scale, class imbalance, and varying weather and illumination conditions. We evaluate 20 state-of-the-art semantic segmentation methods on ASS and observe substantial performance drops, indicating that existing models remain far from practical deployment. The dataset is available at www.agvs-caac.com/ASS/ASS.html. Furthermore, we discuss future directions for airport semantic segmentation and propose a research approach based on imprecise supervised learning, with an initial implementation named SPWSeg (https://github.com/SPWSeg/SPWSeg). Additional experiments show that this research approach generalizes effectively beyond airport scenes.