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arXiv · 2607.18997

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

Tianyang Huang·Alessio Lombardi·Ahmed Elnagar·Ahmed Zalouk·George Paul·Sepehr Najjarpour·Arvid Sigurdsson·Khalid Ismail·Mohamed Ragab·Edlira Vakaj
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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.

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Tianyang Huang, Alessio Lombardi, Ahmed Elnagar, Ahmed Zalouk, George Paul, Sepehr Najjarpour, Arvid Sigurdsson, Khalid Ismail, Mohamed Ragab, Edlira Vakaj. 2026-07-21. Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction. https://arxiv.org/abs/2607.18997

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