During our initial discussions, our client highlighted multiple challenges in enabling efficient, real-time parking for users in urban areas.Â
To solve the client’s parking challenges, our engineers built a smart parking system that runs on edge devices and uses computer vision for real-time detection. Here’s what we implemented:
Data Collection & Annotation
Frames from street footage were extracted and manually labeled using Roboflow to mark parking spaces. Data was augmented to handle various lighting and weather conditions.
Model Training
A lightweight YOLOv8n model was trained using transfer learning for better accuracy on custom classes. The process was managed in the Ultralytics YOLO environment for consistency.
Real-Time Inference
YOLOv8n was paired with ByteTrack to track vehicles across frames. Post-processing logic helped smooth detections and reduce flickering.
Depth Estimation
YOLOv8n was paired with ByteTrack to track vehicles across frames. Post-processing logic helped smooth detections and reduce flickering.
Edge Deployment
The model was optimized and converted to formats like ONNX and TFLite for deployment on Raspberry Pi, enabling fast, low-power inference.
Geo-Spatial Integration
GPS and compass modules captured real-time location and direction, allowing each detection to be mapped accurately on real-world coordinates.
Reduced Costs
By leveraging edge computing, operational costs were reduced by 30%, while labor expenses saw a drop of 20-25%, making the solution more cost-effective.
Improved Accuracy
The integration of YOLOv8n and depth estimation increased detection accuracy by 15-20%, which helped optimize space utilization by 30%, ensuring better use of available parking.
Scalable Solution
The system proved to be highly scalable, with hardware costs 50% lower than traditional cloud-based systems, allowing for future growth without significant expense.
Faster Parking Search
Thanks to mobile app integration, users now experience up to a 40% reduction in the time spent searching for parking, improving the overall user experience.
Optimized Space Use
By integrating GPS and data, the system helped optimize parking space utilization, adding 10-15% more available spots for users.
Better Resource Allocation
Actionable insights generated by the system enabled more efficient resource allocation, improving overall operations by 15-20%.
The solution also supports smart city initiatives by improving urban mobility, reducing congestion, and providing data that can be used for future urban planning.
In the end, we helped the client turn a major urban challenge into a smart, scalable solution. With real-time detection, GPS integration, and edge deployment, the parking experience is now faster, smoother, and way more reliable for users. More than just solving technical issues, the solution supports the bigger goal—making city mobility smarter and everyday commutes less stressful.
Technology & Software
Europe
End to End Project Lifecycle Management
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