
Elephant Detection
Locate, label and count elephants in an image.
Locate, label and count bicycles.
Use This Free TemplateTry PreviewFrom crowded city streets to busy bike paths, this template instantly spots, labels, and tallies every bicycle in view - helping urban planners and businesses make data-driven decisions about cycling infrastructure.
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Urban mobility teams struggle to manually count bikes at key intersections and popular routes, often relying on error-prone clipboard counts or expensive sensor systems. By deploying this bicycle detection template across your existing camera network, you'll capture accurate, real-time bicycle traffic patterns 24/7. Transportation departments use this data to optimize bike lane placement, adjust signal timing, and evaluate the impact of cycling infrastructure investments. Bike-share companies rely on it to track fleet distribution, while retail businesses analyze cyclist foot traffic to determine optimal store locations.
Imagine replacing days of manual counting with automated, round-the-clock bicycle monitoring that delivers precise data within seconds. This template's specialized algorithms detect bikes even in challenging conditions - partially obscured views, low light, or crowded scenes. Beyond simple counting, it provides valuable insights like peak usage times, popular routes, and cycling pattern changes across seasons. For cities investing in bicycle infrastructure, this translates to data-backed decisions that maximize impact while minimizing costs. The template's ability to process multiple video feeds simultaneously means you can scale from single intersection analysis to city-wide bicycle movement studies without adding staff or equipment.