A machine learning-based system designed to detect potholes on roads using advanced image processing techniques.
Potholes pose a serious risk to road safety and vehicle maintenance. This project aims to develop a deep learning-based pothole detection system that can analyze images or videos of roads and identify potholes, enabling timely repairs and improved road conditions.
✅ Real-time Detection – Works with both images and video streams.
✅ High Accuracy – Trained using Convolutional Neural Networks (CNNs).
✅ Easy Integration – Can be deployed in real-world applications like traffic monitoring systems.
This dataset was exported via roboflow.com on January 26, 2024 at 9:04 AM GMT
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The dataset includes 780 images. Pothole are annotated in Tensorflow Object Detection format.
The following pre-processing was applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping)
- Resize to 640x640 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
- 50% probability of horizontal flip
- Randomly crop between 0 and 20 percent of the image
- Random rotation of between -15 and +15 degrees
- Random shear of between -5° to +5° horizontally and -5° to +5° vertically
- Random brigthness adjustment of between -25 and +25 percent
- Random exposure adjustment of between -25 and +25 percent
The pothole detection system is built using YOLOv8, a state-of-the-art object detection model. The model is trained with the following parameters:
- Base Model: YOLOv8
- Training Epochs: 50
- Image Size: 640x640 pixels
- Batch Size: 16
- Early Stopping Patience: 10 epochs
- Optimizer: Auto (chooses the best from [SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp])
- Initial Learning Rate: 0.0001
- Final Learning Rate: 0.01 (calculated as
lr0 * lrf) - Dropout Regularization: 25%
- Device: CUDA (GPU) –
device=0 - Random Seed: 42 (for reproducibility)
The model has been fine-tuned for robustness across various road conditions.