Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction
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Abstract (arXiv)
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 mAP50 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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References · 18
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2025Qwen3-VL Technical Reportcited by 2,534
- 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
- 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
- 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 406
- 2021OCR-free Document Understanding Transformercited by 400
- 2019PubLayNet: largest dataset ever for document layout analysiscited by 349
- 2024SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuningcited by 345
- 2025YOLOv12: Attention-Centric Real-Time Object Detectorscited by 300
- 2022DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysiscited by 146
- 2024DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perceptioncited by 80
- 2021LayoutReader: Pre-training of Text and Layout for Reading Order Detectioncited by 79
6 more
- 2025RF-DETR: Neural Architecture Search for Real-Time Detection Transformerscited by 70
- 2024Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extractioncited by 18
- 2025A Multi-Stage Hybrid Framework for Automated Interpretation of Multi-View Engineering Drawings Using Vision Language Modelcited by 6
- 2022Component Segmentation of Engineering Drawings Using Graph Convolutional Networkscited by 5
- 2025Graph-based Document Structure Analysiscited by 4
- 2025Title block detection and information extraction for enhanced building drawings searchcited by 1
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