BCSNet: Boundary-Centric Fusion and Selective Semantic Pyramid Injection Network for Real-Time Semantic Segmentation

Abstract

Real-time semantic segmentation is a key component of resource-constrained perception systems, such as autonomous driving and robotic navigation, where dense scene understanding must be obtained under strict latency and computation constraints. Although dual-branch architectures provide an effective efficiency-oriented solution, they still suffer from boundary degradation during cross-branch fusion, insufficient multi-scale semantics for small and medium objects, and feature misalignment caused by content-agnostic upsampling near object contours. These limitations become more pronounced at high resolutions, where preserving thin structures and accurate class transitions is often in tension with maintaining high throughput. To address these issues, we present Boundary-Centric Fusion and Selective Semantic Pyramid Injection Network (BCSNet), a real-time segmentation framework that allocates lightweight modeling capacity to boundary-sensitive stages rather than increasing computation uniformly across the network. Specifically, the Boundary-Centric Cross-Branch Fusion and Refinement module learns a shared boundary cue to guide bidirectional feature exchange and local contour refinement with limited overhead. The Semantic Lightweight Feature Pyramid with Selective Injection module provides scale-adaptive semantic cues to the high-resolution stream through a compact pyramid design. The Boundary-Conditioned Region-Adaptive Alignment Upsampling operator further performs content-aware reassembly only within narrow boundary regions, while retaining efficient bilinear interpolation elsewhere. Under a controlled RTX 4090 evaluation protocol on Cityscapes, BCSNet-L achieves 79.5% mIoU at 107.0 FPS, while the lightweight BCSNet-S obtains 76.5% mIoU at 189.0 FPS. On CamVid, BCSNet achieves 77.1% mIoU at 156.8 FPS. These results indicate that BCSNet provides a practical accuracy--efficiency trade-off for high-resolution real-time segmentation, while direct embedded deployment and hardware-specific optimization remain directions for future work.