跨层级交互与方位感知的航拍图像语义分割OA

To address the issues of single-scale feature extraction,detail loss,and blurred boundaries in aerial image semantic segmentation,this paper proposed an aerial image semantic segmentation network with cross-level interaction and orientation awareness.A position awareness module was constructed through a direction-decoupled attention strategy to enhance the model's ability to process spatial directional information;a cross-level interaction module was designed for inter-channel feature interaction and fusion to improve spatial perception,while a channel-spatial attention mechanism was used to enhance feature ex-traction capabilities and alleviate detail blurring issues in complex scenes;finally,a lightweight design was implemented for the segmentation head,removing redundant operations to reduce computational load while ensuring segmentation performance.Experimental results indicate that the proposed network achieves a 1.7%and 1.3%improvement in mean intersection over union on the UAVid and Aeroscapes datasets,respectively,compared to the baseline model SegFormer,demonstrating the network's effective-ness in semantic segmentation under complex conditions such as aerial images.The segmentation accuracy of the Human category improved by 1.8%compared to the baseline model,demonstrating that the net-work proposed in this paper performs excellently in small object segmentation.Compared with several mainstream networks,the method proposed in this paper achieves the highest segmentation accuracy on both datasets,showing superior generalization capability.