Traffic Sign Condition Detector
A YOLOv26-based object detection model for classifying traffic signs by physical condition. Fine-tuned on street-level imagery for use in road infrastructure monitoring and mapping pipelines.
Model Details
- Architecture: YOLOv26
- Base model: Ultralytics/YOLO26
- Task: Object detection + condition classification
- License: MIT
Dataset
Trained on the traffic-sign-detection-znanc-9hhnw dataset from Roboflow. The dataset consists primarily of traffic sign images captured from a distance, representative of typical street view or dashcam footage.
Classes
good, bad, moderate
Performance
The model performs well on street view imagery where signs appear at a distance, which matches the training distribution. It is well-suited for automated road surveys, mapping applications, and infrastructure audits.
Intended Use
- Street view imagery analysis
- Road sign condition auditing
- Smart city and infrastructure monitoring pipelines
Limitations
- Optimized for signs viewed from a distance; close-up or heavily cropped sign images may produce lower confidence
- Trained on English-language traffic sign imagery; performance on other regions may vary
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Model tree for Erpix3lt/traffic-sign-detection
Base model
Ultralytics/YOLO26