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EngJamesO/
pothole-detector

Name: pothole-detector
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Pothole Detection Models

This repository contains trained YOLO models for detecting potholes in road images. The models were developed to assist with road infrastructure monitoring and maintenance planning.

Model Overview

Five YOLO architectures were trained and evaluated for pothole detection:

Model	Parameters	mAP50	mAP50-95	Latency (ms)	FPS
YOLOv9c	25.3M	0.792	0.485	36.3	27.6
YOLOv12n	2.56M	0.785	0.463	13.9	72.1
YOLOv8n	3.01M	0.782	0.465	6.9	144.7
YOLOv11	2.58M	0.779	0.460	9.3	107.0
YOLOv8-FPN	2.21M	0.707	0.396	6.4	155.8

Intended Uses

Primary Use: Automated pothole detection in road images for infrastructure assessment
Secondary Use: Research in object detection, computer vision applications for transportation
Target Users: Road maintenance departments, urban planners, researchers

Model Selection Guide

YOLOv9c: Best overall accuracy (mAP50: 0.792), recommended when accuracy is prioritized over speed
YOLOv8n: Good balance of accuracy and speed, suitable for real-time applications
YOLOv12n: Lightweight with competitive accuracy, ideal for edge deployment
YOLOv11: Efficient architecture with modern optimizations
YOLOv8-FPN: Fastest inference, suitable for high-throughput scenarios

Performance at Different Confidence Thresholds

Based on qualitative evaluation (40 test images, 86 ground truth objects):

YOLOv9c (Recommended)

Conf=0.25: Precision: 0.804, Recall: 0.831, F1: 0.790
Conf=0.50: Precision: 0.846, Recall: 0.723, F1: 0.753
Conf=0.70: Precision: 0.700, Recall: 0.505, F1: 0.558

YOLOv8n

Conf=0.25: Precision: 0.807, Recall: 0.838, F1: 0.791
Conf=0.50: Precision: 0.840, Recall: 0.710, F1: 0.751
Conf=0.70: Precision: 0.600, Recall: 0.437, F1: 0.482

Usage

Installation

pip install ultralytics opencv-python

Python inference

python

from ultralytics import YOLO

# Load model
model = YOLO("yolov9c.pt")

# Run inference
results = model("path/to/image.jpg")

# Visualize results
results[0].show()

Limitations

Models trained on specific road conditions; performance may vary on different terrain or weather conditions
Detection accuracy depends on image quality and resolution
Models may have difficulty with very small or heavily obscured potholes
Performance not validated on night-time or extreme weather images

EngJamesO/pothole-detector