Dataset preparation
The notebook downloads and prepares an annotated pothole dataset, validates the file structure and organizes the images for model training and evaluation.
YOLOv8 · Ultralytics · Object Detection
Pothole Detection with YOLOv8
An end-to-end computer-vision pipeline for detecting potholes in road images, covering dataset preparation, COCO-to-YOLO annotation conversion, custom dataset configuration, model training and inference.
Detection gallery
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Project details
End-to-end object detection workflow
Annotation conversion
COCO-style bounding boxes are transformed into normalized YOLO labels so the dataset can be used directly with the Ultralytics training pipeline.
Dataset configuration
A custom YAML configuration defines the training and validation paths together with the pothole class used by the detector.
Training and inference
YOLOv8 is initialized from pretrained weights, fine-tuned on the custom road dataset and then used to detect and visualize potholes on test images.
Implementation
A reproducible notebook workflow
The project is structured as a sequential Colab workflow: install the required libraries, obtain the data, convert annotations, build the dataset configuration, train the model and inspect inference results.
Python
Ultralytics YOLOv8
OpenCV
Matplotlib
COCO → YOLO conversion
Google Colab