YOLOv8 Architectural Scene Section Recognition Method Based on SimAM-EMA Hybrid Attention Mechanism

Abstract

To overcome the limitations of current methods in architectural scene segmentation accuracy, this study presents a hybrid attention-enhanced YOLOv8 framework and introduces a dedicated building interior profile dataset. The proposed approach extends the YOLOv8 architecture by integrating SimAM (Simple, Parameter-Free Attention Module) to dynamically evaluate neuron significance and refine feature representations. This is coupled with an Efficient Multi-Scale Attention (EMA) module that synergizes local and global attention mechanisms, enabling robust multi-scale feature fusion while maintaining stable weight optimization. To address the scarcity of domain-specific data, a meticulously annotated dataset encompassing common architectural interior elements is developed. Comprehensive evaluations demonstrate that the enhanced model achieves an 89.9% mAP@0.5 on the proposed dataset, outperforming the baseline YOLOv8 with relative improvements of 4.5% in precision, 5.2% in recall, 5.1% in mAP@0.5, and 4.5% in mAP@0.5–0.95. These advancements underscore the efficacy of hybrid attention mechanisms in architectural scene analysis and establish a benchmark dataset to facilitate future research in intelligent building environment interpretation.