Addressing the challenges posed by the complexity of UAV image scenes,significant differences in view angles,and frequent occlusions by foreign objects,an automatic extraction method of UAV multi-view image rail lines based on CBAS_Unet is proposed.On the basis of the traditional U-net network,a parallel Atrous Spatial Pyramid Pooling(ASPP)and Convolutional Block Attention Module(CBAM)are added to enhance the network's capability to capture contextual information across various scales,thereby significantly boosting the segmentation performance of rails.High-precision extraction of complete rail vector lines is then achieved by pixel grouping using RANSAC least squares fitting and chaining of neighboring rail lines.The experimental results show that,compared with the two classical models of U-net and Deeplab v3+,the proposed method achieves an increase in the intersection and merger ratio for rail segmentation of multi-view UAV images by 2.09％and 1.98％,and the score value is improved by 1.50 and 1.42,respectively.The completeness of rail line extraction reaches 90.7％,surpassing the 83.3％achieved by the U-net model.The average error in rail line extraction is approximately 0.58 pixels,with the median error of approximately 0.77 pixels,enabling sub-pixel-level rail line extraction.This method fulfills the requirements for automation,comprehensive and high-precision extraction of rail lines from UAV multi-view images.
关键词
无人机影像/钢轨线提取/空洞空间金字塔/注意力机制/U-net
Key words
UAV image/Rail line extraction/Atrous Spatial Pyramid/Attention mechanism/U-net
维普
万方数据