A Real‐Time Semantic Segmentation Network with Boundary‐Focused and Multi‐Scale Context Fusion

Abstract

ABSTRACT Real‐time semantic segmentation is crucial for applications including autonomous driving and augmented reality. While current real‐time semantic segmentation methods achieve a balance between accuracy and speed, an adequate capture of boundary details remains a challenge for many models. Furthermore, as deep learning networks become increasingly complex, certain approaches encounter challenges, including excessive computational overhead and numerous parameters when capturing multi‐scale contextual features. To address these limitations, the boundary‐focused and multi‐scale context fusion network (BFMSNet) is proposed, a lightweight real‐time semantic segmentation model that enhances boundary perception and contextual understanding. A boundary refinement module is designed, which utilizes multi‐level feature fusion and a gating mechanism to precisely capture edge details in complex scenes and achieve pixel‐level boundary alignment and optimization. Furthermore, a hybrid boundary loss is introduced, combining region and boundary supervision signals to effectively guide the network's focus on challenging regions, thereby improving training stability and segmentation accuracy. To reduce model complexity, a lightweight multi‐scale fusion module is implemented based on the multi‐scale frequency‐domain characteristics of wavelet convolution. This module balances context information extraction and computational efficiency, reducing parameters while maintaining feature representation. Experimental results on the Cityscapes and CamVid datasets demonstrate that BFMSNet achieves mIoU of 78.53% and 76.24%, while maintaining real‐time inference speeds of 86.25 FPS and 143.70 FPS, respectively. Preliminary tests indicate that the BFMSNet algorithm effectively balances accuracy and speed requirements.