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

2026-07-21

AI Summary

This study addresses the long-standing reliance on manual annotation for information extraction from architecture, engineering, and construction (AEC) drawings and the lack of effective research on layout detection in such domain-specific documents. To bridge this gap, the authors introduce the first AEC-focused drawing layout dataset and conduct a systematic evaluation of various deep learning models. Their analysis reveals, for the first time, a “domain interference” issue wherein general-purpose document understanding models underperform on AEC drawings. To overcome this limitation, they propose a novel approach combining the RF-DETR object detection model with the multimodal vision-language model Qwen3-VL. Experimental results demonstrate that RF-DETR achieves a mAP50 of 0.949 in layout detection, while Qwen3-VL attains an F1-score of 0.911 in information extraction, significantly outperforming existing general-purpose models and establishing both as state-of-the-art architectures for AEC drawing comprehension.

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.

Problem

• Information Extraction
• Layout Detection
• AEC drawings
• domain interference
• engineering drawings

Innovation

• AEC drawings
• layout detection
• information extraction
• domain interference
• deep learning benchmarking