A real-time semantic segmentation network leveraging spatial and contextual features for enhanced scene understanding

Real-time semantic segmentation of images requires both rich contextual and accurate spatial information. However, Multiple downsampling in deep convolutional neural networks often lead to loss of such information, resulting in reduced segmentation accuracy. To address the above problems, we propose SPCONet, a lightweight real-time semantic segmentation network that integrates spatial and contextual features. The network incorporates three key modules: (1) a Spatial Feature Aggregation Module (SFAM) that captures fine spatial details from shallow layers using spatially separable convolutions with multiple kernel sizes; (2) a Contextual Information Retrieval Module (CIRM) that extracts semantic context from deeper layers using dynamic convolution; (3) an Attention Fusion Module (AFM) that combines spatial and contextual features via local and global attention mechanisms. Quantitative experiments show that SPCONet achieves 77.5% and 75.3% mIoU at 74 FPS and 82 FPS on the Cityscapes and CamVid datasets, respectively. These results suggest that SPCONet provides an effective balance between segmentation accuracy and real-time inference capability.