Abstract:

Traditional road extraction methods exhibit instability when utilizing data information for processing road images of different types and scales. One major reason for this is that these methods fail to effectively integrate local and global features, demonstrating a weak ability in extracting road details and semantic information. To address this issue, this paper proposes a new road extraction method that employs spatial feature fusion and enhancement strategies. Specifically, the spatial feature fusion module uses graph convolution and local feature extraction to effectively combine global and local features. The spatial feature strength module, on the other hand, applies an attention mechanism to weigh features along both spatial and channel dimensions, enhancing the model’s ability to perceive features, thereby improving its adaptability to road images of different scales. This paper conducted experimental validation of this method on multiple datasets and compared it with existing approaches. The experimental results demonstrate that this method significantly improves performance in road segmentation tasks, offering high robustness and generality, making it suitable for road image datasets of various types and scales.

Key words: spatial feature fusion, attention mechanism, graph convolution, image processing