Laborelec, a research and expertise center in electrical power technology and a subsidiary of Engie, needed to streamline how it counts and classifies street lamps in urban areas. This task, essential for tender preparation and maintenance planning, was technically feasible using video footage from service car cameras. However, manual extraction of this information proved too costly and time-intensive to scale.
To address this, Laborelec partnered with Sagacify to develop an AI solution capable of automatically detecting, classifying, and geolocating street lamps from video streams. The collaboration also focused on minimizing labeling costs through a custom data annotation tool and an active learning system designed to maximize efficiency.
Sagacify developed a machine learning system that processes in-car video footage to extract the location, type, and count of street lamps. The solution included:
Only 700 images, labeled in under 4 hours, were required to train the final model—thanks to the data efficiency of the active learning system.
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