P-001 · 2025 · Hackathon
Broken-tile detection with YOLOv8
A computer-vision prototype built during the 2025 ChefTreff AI Hackathon, connecting a trained defect detector to an image-upload API and a simple web interface.
The challenge
The project explored how images taken on a construction site could help identify damaged roof tiles. The task was to detect visible defects in an image and make the prediction accessible through a small application, within the 24-hour hackathon.
Data and model
The workflow includes image collection and preprocessing notebooks, annotation in CVAT and labels exported in YOLO format. An Ultralytics YOLOv8 model was trained to detect broken tiles. The repository includes the trained checkpoint and prediction outputs.
From model to application
A FastAPI endpoint accepts an uploaded image, converts it to RGB and runs the detector. The implemented response reports whether a broken tile was detected and how many detections were returned. A lightweight HTML client provides the upload interface, while Docker packages the application and its dependencies.
What the prototype demonstrates
The project connects data preparation, object detection and an application interface in one workflow. It is a hackathon prototype: the presence of a trained model and demo does not establish readiness for construction-site use. Broader validation across lighting conditions, viewpoints and types of damage would be needed.