## 2 Related Work  
In earlier studies, significant progress has been made in semantic segmentation for structured environments like urban roads and well-defined landscapes. Models such as UNet [10], ENet [11], BiSeNetv1 [12], and BiSeNetv2 [13] have become popular for road segmentation tasks, especially in urban settings. However, these models face notable limitations when applied to unstructured rural road environments. UNet is a well-known encoder-decoder architecture primarily used for biomedical image segmentation. It relies on skip connections between the encoder and decoder, which help recover spatial details lost during downsampling. However, UNet's pooling operations and upsampling methods often struggle with complex, unstructured environments like rural roads, where the road shapes and boundaries are irregular. ENet performs well in real-time applications, but its lightweight design compromises segmentation accuracy and may not be comparable to more complex models when faced with complex country roads and small objects. BiSeNetv1 uses a two-pathway architecture to balance spatial detail extraction with contextual information. Although it performs well in urban road segmentation, it struggles with rural roads' long and narrow structures, as its spatial pathway cannot fully retain crucial geometric information. BiSeNetv2 improves upon its predecessor by optimizing for lightweight, real-time segmentation. However, this reduction in computational complexity comes at the cost of reduced accuracy, particularly for small objects and complex scenes, making it less effective in unstructured rural environments. In recent years, deep convolutional neural networks have made significant advances in computer vision, particularly in tasks like image classification, object detection, and semantic segmentation, where they have shown outstanding performance [13; 14]. The advent of deep learning has provided an effective approach to addressing the challenges of complex road scene recognition and parsing [15]. [PERSON] et al. [16] developed an improved DeepLabV3+ visual navigation path recognition method proposed for semantic segmentation, effectively realized in the environment of the Pitaya Orchard. However, the computational complexity of the model is high and faces difficulties in practical applications. [PERSON] et al. [17] proposed a Deep Dual-resolution Road Scene Segmentation Network Decoupled Dynamic Filter and Squeeze-Excitation (DDF&SE-DDRNet) based on decoupled dynamic filtering and squeezed excitation, which reduces the number of network parameters while still having satisfactory segmentation results. Similarly, there are problems of high model complexity and excessive computational effort. [PERSON], [PERSON], et al. [18] introduced a parallel complementary network (PCNet), which was proposed to obtain better results on public datasets by utilizing the inverted residual structure while having a large feeling field. Although the parallel network architecture with multiple branches enhances the feature extraction, it also increases the complexity of the model. Despite these advancements, existing studies utilizing convolutional neural networks for semantic segmentation still face challenges such as high parameter count, computational complexity, and suboptimal inference speed. Furthermore, there remains room for improvement in the utilization of contextual and global information in image processing, which has a direct impact on the segmentation accuracy of complex scenes. There are three major challenges in achieving semantic segmentation of rural roads. First, the complexity of rural road scene images poses a formidable obstacle. The intricate environment, undefined boundaries, uneven road surfaces, occlusions, and numerous obstacles make rural road images much more complex than urban road scenes. Consequently, this complexity significantly heightens the difficulty of semantic segmentation tasks. Second, the variability of rural road scene images presents another challenge.