Shared Bicycle Improper Parking Detection
Algorithm Introduction
Utilizing visual analysis technology to identify improperly parked shared bicycles within designated monitoring areas, providing real-time location and quantity feedback to assist maintenance personnel in efficiently regulating parking order. The system detects shared bicycles parked 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
-
Urban Roadways
Through road surveillance equipment, the algorithm promptly identifies visually detectable illegally parked shared bicycles to prevent them from occupying pedestrian or traffic roads and causing congestion. -
Subway Stations
The algorithm focuses on designated areas around entrances and exits of high-traffic transportation hubs , capturing illegally parked bicycles in real time to prevent accumulation that obstructs pedestrian flow, ensuring efficient and smooth subway transfers. -
Parks
The algorithm focuses on monitoring predefined areas such as entrances and pathways, swiftly identifying illegally parked shared bicycles. This approach safeguards the park's landscape while creating a safe and comfortable environment for visitors. -
Business Districts
The algorithm monitors preset zones in high-traffic areas with frequent bicycle use to swiftly locate illegally parked bicycles, preventing disorderly parking from undermining the city's image and disrupting normal business operations.
FAQ
-
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.
-
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.
-
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.
