AI Blueprint Classifier for Civil Engineers to reduce marking errors up to 90%
Manual blueprint analysis in construction projects often leads to time-consuming tasks and errors. We built an AI blueprint classifier using computer vision and deep learning to automate the highlighting and coloring of plan sheets, eliminating manual intervention and freeing engineers and designers to focus on higher-level tasks.
Key Features
Accurate Blueprint Region Identification
Our system employs sophisticated edge detection algorithms to identify the boundaries of different regions on blueprints accurately. This ensures that the system precisely delineates areas for labeling and coloring, preventing errors and inconsistencies.
Precise Number and Arrow Detection
We utilized state-of-the-art object detection models to detect numbers and arrows scattered across plan sheets for identifying specific regions and features, such as pavements, gutters, and utility lines.
Automated Labeling and Coloring
Our system accurately assigns labels and colors to different regions by cross-referencing detected numbers with the Construction Notes, ensuring that the final plan sheet is visually clear and informative.
Flexible Customization Options
We provide a high degree of customization for users to choose from a variety of color palettes, labeling styles, and other preferences. This ensures that the final output aligns with their desired aesthetic and standards.
Seamless Integration with CAD Software
Our team seamlessly integrated the solution with existing CAD software, providing a smooth workflow for engineers and designers. This eliminated the need for manual data transfer and ensured that the automated process fit seamlessly into their existing design tools.
Benefits
The solution automated the time-consuming process of highlighting and coloring plan sheets, reducing project completion time by up to 30%.
Our AI-powered system ensured consistent and accurate labeling, reducing the risk of human errors by up to 90%, and the likelihood of costly mistakes, enhancing quality.
By streamlining the design process through automation, we optimized resource allocation and reduced labor costs by up to 15%.
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