Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction

Abstract:Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detection, a vital 'middleware' organizing graphical and textual hierarchies, is underexplored. General document layout models, optimized for text-centric content, lack validation on engineering drawings. This study constructs a custom AEC-specific layouts dataset and benchmarks five deep learning architectures. RF-DETR achieves state-of-the-art performance with an $mAP_{50}$ of 0.949, while the Vision-Language Model Qwen3-VL attains a leading F1-score of 0.911. Conversely, models pre-trained on general document datasets suffer from "domain interference", causing performance degradation. This establishes a robust technical foundation for automated IE in AEC.

Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

arXiv:2607.18997 [cs.CV]

(or arXiv:2607.18997v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.18997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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