@misc{9223713,
  abstract     = {{Fire-safety documentation is an essential part of regular inspections during the building operations and maintenance phase. As help, service drawings exist to detail the positions of various fire-safety devices and their addresses, as well as fire zone coverage throughout the floors and sectors of a building. Automatically extracting this information would be immensely helpful towards, for example, maintaining up-to-date documentation or integration onto digital systems. To that end, this thesis adapts multiple computer vision techniques in order to extract pertinent information: (1) a Keypoint R-CNN model is trained on a custom dataset to detect the symbols of fire devices along with their installation positions, (2) OCR is used to parse addresses and (3) a region merging procedure is implemented to segment fire zones. Additionally, symbols are also assigned to their addresses using the Hungarian algorithm, with Euclidean distance as base cost.

For the symbol detection, we find that crucial model settings had to be changed in order to tailor the model for the domain and achieve satisfactory performance. High accuracy is achieved despite major class-imbalances in the dataset, but the model fails in all cases to detect the true installation positions of devices, whenever these are indicated by an auxiliary line. Assigning symbols and addresses to each other based on distance works well in most cases, while having a few edge cases that are impossible to amend with our method alone, due to inherent quirks in the service drawings. Using region merging for the fire zone segmentation gives us high recall, but low precision. The low precision is mainly due to the oversegmentation step of region merging, and partly due to the existence of hard-to-ignore false positives in most service drawings. Overall, the results across all tasks are promising while still leaving plenty of room for improvement, or alternative approaches, in further work.}},
  author       = {{Ekstrand, Julius and Truong, Victor}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Computer Vision Approaches for Extracting Fire-Safety Information from Service Drawings}},
  year         = {{2026}},
}