Abstract: Graphic element recognition constitutes a critical step in the intelligent analysis and compliance review of architectural drawings.Although the DETR(Detection Transformer) framework demonstrates significant potential in general object detection,notable challenges persist when applied to complex CAD graphic element recognition tasks.Specifically,DETR's limitations primarily stem from two aspects:1) Its feature extraction mechanism exhibits insufficient adaptability to scale variations among graphic elements,failing to simultaneously capture fine details of small components and large-scale structural information effectively,which results in missed detections of small elements and ambiguous boundary recognition for large ones;2) The sparse spatial distribution of informative pixels within graphic elements hinders efficient attention focusing on key regions,making the mechanism susceptible to substantial background interference.To address these issues,three key improvements to the DETR framework are proposed.A Swin Transformer based multi-scale feature extraction module is adopted to enhance robust representational capability for elements of varying sizes.A spatially sparse sampling attention mechanism is introduced to optimize the sampling efficiency of informative features and improve perception performance in critical areas.A contrastive denoising training strategy is integrated to strengthen model localization capability for sparse informative features.Experimental results on a real-world architectural dataset demonstrate that the proposed method achieves a mAP_[0.5-0.95] of 0.675,significantly outperforming existing mainstream object detection models.This validates the proposed method's effectiveness and practical engineering value for architectural graphic element recognition task.

Key words: Graphic element recognition, Computer vision, Object detection, Computer-Aided Design(CAD)