Global context-aware multi-scale feature iterative refinement for aviation-road traffic semantic segmentation

Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars. The State-of-the-Art (SOTA) semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance. However, when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic, they still face great challenges and there is little related research. Addressing the above issue, this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes. First, a novel decoding-recoding multi-scale feature iterative refinement structure is proposed, which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes, while using a fully convolutional structure to ensure the lightweight and real-time requirements. Second, a well-designed cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception, which can effectively capture the long-range dependencies and multi-scale global context information of different scenes, thereby achieving fine-grained semantic segmentation. The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes. The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes. Its segmentation accuracy surpasses that of the SOTA methods, which meets the real-time requirements.