Development of an automated race number recognition system for motorcycle racing – end-to-end inference pipeline with YOLO-based detection, OCR fine-tuning, and .NET integration.
Client/Company/Industry
Christian Engelhardt Softwareentwicklung
Duration
6 months
Product
Software
Expertise
Software Development
A motorsport software developer needed an automated pipeline for detecting race numbers on motorcycles. The solution had to work reliably under real racing conditions - with variable lighting, motion blur, and diverse typefaces on number plates - and integrate into an existing .NET application on Windows.
The main challenge was the variability of input data: race numbers on motorcycles appear in different sizes, angles, typefaces, and lighting conditions. Models had to be robust against this variability without relying on specialised GPU hardware.
Fine-tuning OCR models on motorsport-specific typefaces and image conditions required careful data curation and iterative evaluation to ensure reliable digit recognition in real-world use.
Programming Languages
Python, C#
Technologies
YOLO, TensorRT, ONNX, OpenCV, PythonNET
Race number recognition inference pipeline showing detection bounding box and OCR output.
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The result is a complete end-to-end race number recognition pipeline that runs stably on CPU and integrates seamlessly into the client's existing .NET application. The combination of YOLO detection and a fine-tuned OCR model delivers reliable results under real race conditions.
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