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vinothvikas1987/
pothole-detection-yolov8

Name: pothole-detection-yolov8
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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

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

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