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Updated: Jan 27, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision.

Francisco Troncoso-Pastoriza1, Pablo Eguía-Oller2, Rebeca P Díaz-Redondo3

  • 1School of Industrial Engineering, University of Vigo, Campus Universitario, 36310 Vigo, Spain. ftroncoso@uvigo.es.

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Summary

This study enhances computer vision for detecting building lighting, improving lamp inventory accuracy. New methods precisely locate and identify lighting elements using building information models and advanced filtering.

Keywords:
building information modellingbuilding lightinglamp detectionpose estimation

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Area of Science:

  • Computer Vision
  • Building Information Modeling (BIM)
  • Robotics

Background:

  • Accurate inventory of building lighting is crucial for maintenance and energy efficiency.
  • Previous computer vision methods for lamp detection faced limitations in precision and completeness.
  • Integrating geometric data from Building Information Modeling (BIM) offers potential for improved localization.

Purpose of the Study:

  • To enhance computer vision systems for precise detection and inventory of building lighting elements.
  • To improve the accuracy of lamp location and state identification.
  • To increase the overall number of successful lighting element detections.

Main Methods:

  • Implemented orientation constraints based on BIM geometric data in pose optimization.
  • Introduced a reprojection error filtering step to remove erroneous pose detections.
  • Utilized a framework from previous work, incorporating these two novel modifications.
  • Tested the enhanced system on over 30,000 images across five diverse case studies.

Main Results:

  • Significant improvement in the number of lighting element detections.
  • Increased percentage of correctly identified lamp models and states.
  • Reduced distance between detected and reference positions, indicating higher localization accuracy.
  • Demonstrated effectiveness across multiple real-world building scenarios.

Conclusions:

  • The proposed enhancements effectively improve computer vision-based lighting element detection in buildings.
  • The integration of BIM geometric constraints and advanced filtering leads to more accurate and comprehensive inventories.
  • This approach offers a robust solution for precise identification and localization of lighting infrastructure.