A team of 50 Omdena AI engineers collaborated in this high-impact 2-months innovation project to identify and construct digital objects from floor plans using computer vision.

The problem

To recognize floor plan elements in a layout requires manual labor to draw the different elements over the image. The goal of this project has been to improve the efficiency of this manual effort by automatically identifying the relevant types of objects present using state-of-the-art deep learning and computer vision approaches.

The project outcomes

The team built computer vision models to digitize the floor plan from architectural blueprints. The team successfully applied the following methods in achieving the tasks:

• Object detection.
• Image segmentation using Mask RCNN.
• Improved Optical character recognition (OCR) using the provided datasets
• Identifying languages other than English on floor plans.

Data

Archilyse provided a large set of bitmap images of different sizes and dimensions, along with the bounding boxes of the relevant type of elements manually drawn. Examples of those are walls, columns, railings, kitchen furniture, shower, windows, doors, bathtub, bedroom area, kitchen area, etc.