Real-time UAV image segmentation algorithm with enhanced contextual feature interaction

A real-time UAV image segmentation algorithm with enhanced contextual feature interaction is proposed to address the problem of target omission and incompleteness in segmentation results due to the lack of global information interaction in lightweight algorithms for semantic segmentation tasks of UAV images. The approach uses a two-branch structure. To encode the channel and spatial information, global average pooling in various directions was used. This preserves the correct position information and increases the attention to the local detail information in the image. Secondly, a global perceptual extraction module was constructed by using the position-aware circular convolution and spatial weighting, which achieves the global contextual information capture; Finally, the weighting operation is applied to the features of different scales for the fusion, which reduces the information loss in the fusion process and the computation of the algorithm. The UAVid and AeroScapes datasets are used to validate the algorithm. The results indicate that the mean intersection over union (mIoU) achieved 66.5% and 63.0%, respectively, marking a 2.6% and 2.2% improvement over BiSeNet V2. The segmentation speeds reached 79.9 and 71.4 frames per second, respectively, showing an increase of 8.3 and 6.9 frames per second compared to BiSeNet V2. This method ensures real-time segmentation speed while delivering satisfactory segmentation accuracy.