Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction
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arXiv
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| Format: | Preprint |
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2025
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| author | Trzeciakiewicz, Mariusz Barreiro, Aleixo Cambeiro Gard, Niklas Hilsmann, Anna Eisert, Peter |
| author_facet | Trzeciakiewicz, Mariusz Barreiro, Aleixo Cambeiro Gard, Niklas Hilsmann, Anna Eisert, Peter |
| contents | Digitalization in the construction industry has become essential, enabling centralized, easy access to all relevant information of a building. Automated systems can facilitate the timely and resource-efficient documentation of changes, which is crucial for key processes such as progress tracking and quality control. This paper presents a method for image-based automated drywall analysis enabling construction progress and quality assessment through on-site camera systems. Our proposed solution integrates a deep learning-based instance segmentation model to detect and classify various drywall elements with an analysis module to cluster individual wall segments, estimate camera perspective distortions, and apply the corresponding corrections. This system extracts valuable information from images, enabling more accurate progress tracking and quality assessment on construction sites. Our main contributions include a fully automated pipeline for drywall analysis, improving instance segmentation accuracy through architecture modifications and targeted data augmentation, and a novel algorithm to extract important information from the segmentation results. Our modified model, enhanced with data augmentation, achieves significantly higher accuracy compared to other architectures, offering more detailed and precise information than existing approaches. Combined with the proposed drywall analysis steps, it enables the reliable automation of construction progress and quality assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03422 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction Trzeciakiewicz, Mariusz Barreiro, Aleixo Cambeiro Gard, Niklas Hilsmann, Anna Eisert, Peter Computer Vision and Pattern Recognition Digitalization in the construction industry has become essential, enabling centralized, easy access to all relevant information of a building. Automated systems can facilitate the timely and resource-efficient documentation of changes, which is crucial for key processes such as progress tracking and quality control. This paper presents a method for image-based automated drywall analysis enabling construction progress and quality assessment through on-site camera systems. Our proposed solution integrates a deep learning-based instance segmentation model to detect and classify various drywall elements with an analysis module to cluster individual wall segments, estimate camera perspective distortions, and apply the corresponding corrections. This system extracts valuable information from images, enabling more accurate progress tracking and quality assessment on construction sites. Our main contributions include a fully automated pipeline for drywall analysis, improving instance segmentation accuracy through architecture modifications and targeted data augmentation, and a novel algorithm to extract important information from the segmentation results. Our modified model, enhanced with data augmentation, achieves significantly higher accuracy compared to other architectures, offering more detailed and precise information than existing approaches. Combined with the proposed drywall analysis steps, it enables the reliable automation of construction progress and quality assessment. |
| title | Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.03422 |