UAV aerial image segmentation method based on improved U-Net

With the rapid development of UAV technology, the demand for image recognition segmentation acquired from the air is gradually growing. In this paper, we propose an improved U-Net architecture designed to enhance the effectiveness of UAV image segmentation tasks. Firstly, the traditional convolution module is replaced by a depth-separable convolution to reduce the number of parameters and computational cost and to improve the lightweight performance of the model; secondly, an Axial Attention mechanism is introduced to enhance the model's ability to capture long-range dependencies and spatial information; and finally, the Focal Loss is employed to solve the category imbalance problem. We conducted extensive experimental evaluations on the Aeroscapes dataset containing images captured by UAVs from different altitudes, and the experimental results show that the improved U-Net model exhibits significant improvements in the UAV image segmentation task compared to the traditional U-Net model. Our model improves 6.7% in miou and 2.7% in precision by 2.9% and 3.3% in F1 score. Thus, the method proposed in this paper has potential applications for improving the automation and accuracy of aerial image analysis.