Progressive Terrain Segmentation Network for Navigable Areas With Global Sparsity-Entropy and Fusion-Awareness

Precise segmentation of safe navigable areas is crucial for wild scene parsing in self-driving systems. Previous research has demonstrated that effective feature representation enhances model performance, yet few methods thoroughly explore the complementary relationships between features at different scales in complex wild environments. In this paper, we propose a Progressive Terrain Segmentation Network (PTSNet) for the segmentation of navigable areas, which introduces global contextual information as prior knowledge for fusion and delivers robust feature representation by progressively exploring the complementary relationships between multi-scale features from both spatial and channel perspectives. PTSNet consists of two main components: the Global Sparsity-Entropy Module (GSEM) and the Fusion-Awareness Module (FAM). The GSEM, based on self-attention, employs Top-K sparsity and entropy refinement to effectively capture global semantic information with long-range dependencies, and the information derived from GSEM act as prior knowledge to guide feature fusion. Additionally, we propose the FAM, which consists of the Attention Aggregation Unit (AAU) and the Contribution-aware Unit (CAU), to explore complementary relationships in multi-scale feature interactions and obtain sufficient scene information. Extensive experiments conducted on various wild datasets demonstrate that PTSNet outperforms state-of-the-art methods in accurately segmenting navigable areas, offering a new solution for the safe operation of self-driving systems in wild environments. The code will be available at https://github.com/lv881314/PTSNet