Shared Bicycle Inventory Counting
Algorithm Introduction
Utilizing visual analysis technology to detect the quantity of shared bicycles placed outside user-defined regions of interest (ROI) on road surfaces, with optimal performance in outdoor environments.
- ● Lighting conditions: Daytime outdoor environments with normal illumination
- ● Image requirements: Optimal detection performance at 1920×1080 resolution
- ● Target size: Visually identifiable by human eye
Application Value
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Urban Roadways
The algorithm monitors predefined regions of interest (ROIs) such as the boundaries between motor vehicle lanes and non-motor vehicle lanes, as well as intersections and corners, counting the number of shared bicycles and preventing parking that causes traffic congestion. -
Subway Stations
The algorithm allows real-time monitoring of designated ROI areas around entrances and exits of stations. This enables rapid counting of shared bicycles and timely detection of vehicle congestion and allows dispatch personnel to respond swiftly, ensuring unimpeded pedestrian flow. -
Parks
The algorithm accurately counts the number of parked shared bicycles in high-traffic areas such as entrances and along walkways, preventing excessive bicycles from impacting the park's order and visitor experience. -
Business Districts
The algorithm counts the number of shared bicycles within predefined zones around office areas and commercial districts, providing data support for operators to allocate vehicles. This prevents vehicle accumulation from impacting the area's business image and street order.
FAQ
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Algorithm AccuracyAll algorithms published on the website claim accuracies above 90 %. However, real-world performance drops can occur for the following reasons:
(1) Poor imaging quality, such as
• Strong light, backlight, nighttime, rain, snow, or fog degrading image quality
• Low resolution, motion blur, lens contamination, compression artifacts, or sensor noise
• Targets being partially or fully occluded (common in object detection, tracking, and pose estimation)
(2) The website provides two broad classes of algorithms: general-purpose and long-tail (rare scenes, uncommon object categories, or insufficient training data). Long-tail algorithms typically exhibit weaker generalization.
(3) Accuracy is not guaranteed in boundary or extreme scenarios.
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Deployment & InferenceWe offer multiple deployment formats—Models, Applets and SDKs.
Compatibility has been verified with more than ten domestic chip vendors, including Huawei Ascend, Iluvatar, and Denglin, ensuring full support for China-made CPUs, GPUs, and NPUs to meet high-grade IT innovation requirements.
For each hardware configuration, we select and deploy a high-accuracy model whose parameter count is optimally matched to the available compute power.
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How to Customize an AlgorithmAll algorithms showcased on the website come with ready-to-use models and corresponding application examples. If you need further optimization or customization, choose one of the following paths:
(1) Standard Customization (highest accuracy, longer lead time)
Requirements discussion → collect valid data (≥1 000 images or ≥100 video clips from your scenario) → custom algorithm development & deployment → acceptance testing
(2) Rapid Implementation (Monolith：https://monolith.sensefoundry.cn/)
Monolith provides an intuitive, web-based interface that requires no deep AI expertise. In as little as 30 minutes you can upload data, leverage smart annotation, train, and deploy a high-performance vision model end-to-end—dramatically shortening the algorithm production cycle.
