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vinothvikas1987/pothole-detection-yolov8

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Model Card

Pothole & Road Distress Detection (YOLOv8s)

Fine-tuned YOLOv8s model for detecting 5 types of road surface distress from drone and dashcam imagery.

Training Data Labels

[image] Distribution of bounding boxes across classes, positions, and sizes in the training set

[image] Example training images with augmented bounding boxes during training

Realistic Capabilities

Where This Model Works Best

Scenario	Performance	Notes
Drone/ aerial road survey	✅ Good	Model trained on Japan/India drone datasets
Dashcam footage	✅ Good	Standard road-facing camera angles
Well-lit conditions	✅ Good	Training data is mostly daylight
Pothole detection	mAP50=0.782	Best-performing class — distinct visual features
Alligator crack detection	mAP50=0.671	Moderate — interconnected cracks are distinctive
Longitudinal/ Transverse cracks	mAP50~0.57	Harder — thin features, requires good resolution
"Other" class (manholes, patches)	mAP50=0.494	Weakest — too diverse, consider ignoring

Limitations

Limitation	Why
Poor in heavy rain/ fog	Training data lacks adverse weather
Night detection degraded	No night-time training images
Thin hairline cracks	May miss cracks thinner than ~5px at 640px input
Class imbalance	"Other" class has few examples (965 instances vs 3890 for Longitudinal)
Overlapping cracks	Struggles when multiple crack types intersect
Very wide potholes (>5m)	Rare in training data

Recommended Use

—Automated road inspection from drones for municipal maintenance
—Pre-screening dashcam footage for pothole alerts
—Asset management — quantifying crack density per road segment
—NOT recommended for safety-critical real-time braking systems (use as advisory only)

Model Performance

Metric	Value
mAP@0.5	0.629
mAP@0.5:0.95	0.345
Precision	0.662
Recall	0.583

Per-Class Performance

Class	mAP50	Precision	Recall
Longitudinal Crack	0.571	0.618	0.537
Transverse Crack	0.562	0.631	0.521
Alligator Crack	0.671	0.663	0.639
Pothole	0.782	0.706	0.740
Other	0.494	0.628	0.451

Classes

0: Longitudinal Crack — cracks parallel to road direction (thin, linear)
1: Transverse Crack   — cracks across the road (thin, linear)
2: Alligator Crack    — interconnected web of cracks (fatigue cracking)
3: Pothole            — bowl-shaped depressions (most detectable)
4: Other              — manholes, patches, oil spills, road markings

Quick Usage

python

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("road_image.jpg", conf=0.25, save=True)
# Results saved to runs/detect/predict/

bash

# CLI
yolo predict model=best.pt source=video.mp4 conf=0.25
yolo predict model=best.onnx source=image.jpg conf=0.25

Adjusting Confidence Threshold

python

# For pothole detection (high precision) — use conf=0.4
results = model.predict("image.jpg", conf=0.4)

# For crack screening (high recall, more false positives) — use conf=0.15
results = model.predict("image.jpg", conf=0.15)

Model Files

File	Size	Format
`best.pt`	67 MB	PyTorch (Ultralytics YOLO)
`best.onnx`	45 MB	ONNX (cross-platform)
`best.torchscript`	45 MB	TorchScript (C++ inference)

Training Details

—Base model: YOLOv8s (COCO pretrained, 11.1M params)
—Dataset: RDD (Road Damage Dataset) — 26,869 training images from Japan, India, Czech Republic
—Epochs: 76 (stopped by Kaggle 9h time limit, still improving)
—Input size: 640×640
—Hardware: NVIDIA Tesla T4 (16GB) × 2
—Batch: 64
—Training time: ~9 hours

Training script: `training/train_improved.py`

Citation

bibtex

@misc{pothole-detection-yolov8-2026,
  author = {vinothvikas1987},
  title = {Pothole and Road Distress Detection with YOLOv8s},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {https://huggingface.co/vinothvikas1987/pothole-detection-yolov8}
}

License

Apache 2.0