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Road Pothole Detection With Machine Learning And Computer Vision

Azati Labs developed a proof-of-concept prototype using machine learning and computer vision to detect road defects from images and videos. The solution can assist municipal governments in automating road defect detection and repair cost estimation while also helping automotive manufacturers reduce maintenance costs for smart vehicles.

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85-92%

accuracy of pothole and road defect detection on annotated datasets

60-75%

reduction in manual inspection time when using the prototype

70-80%

success rate of video frame processing without loss of defect detection quality

All Technologies Used

Python
Python
Caffe
Caffe
OpenCV
OpenCV

Motivation

Manual inspection of roads is time-consuming, inconsistent, and prone to errors. The system was developed to automatically detect potholes and other road surface defects from images and videos, reducing inspection effort, improving accuracy in defect identification, and enabling more efficient maintenance planning.

Main Challenges

Challenge 01
Lack of High-Quality Data

No publicly available datasets existed with sufficient images of potholes or road defects. Using open-source images yielded poor model performance, so the team collected live data from Belarusian roads, which required significant planning and fieldwork.

#1
Challenge 02
Data Inconsistency

Captured videos and images had variations in lighting, weather, and camera angles, causing inconsistencies. Each frame had to be manually mapped and validated to ensure usable data for training, making the process labor-intensive.

#2
Challenge 03
Low-Quality Footage

Many video clips were recorded in low resolution or poor quality. These had to be filtered out, as using them would distort model training. Handling high frame-rate videos required splitting them into keyframes and individually labeling each frame, adding complexity and time to the process.

#3

Our Approach

Live Data Collection
The team collected road footage from various vehicles to gather real-world images and videos, ensuring diverse environmental conditions for robust model training.
Keyframe Analysis
Videos were split into keyframes, each manually mapped to accurately identify potholes and other defects, creating a high-quality training dataset.
Custom Machine Learning Model
A specialized object detection model was trained from scratch using the curated dataset, optimized for road defect detection.
Prototype Development
The prototype processes images or video clips, outlines detected defects with bounding boxes, and outputs annotated images or videos. Videos are reassembled after frame-by-frame analysis.

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Solution

01

Image and Video Processing

The prototype processes both images and videos, splitting videos into individual frames to analyze each for potential road defects. This ensures no defect is missed, even in moving footage, and enables accurate detection across different conditions.
Key capabilities:
  • Frame extraction from video clips
  • Processing of static images
  • Support for high-resolution and low-resolution inputs
  • Consistent analysis across frames
02

Custom Machine Learning Model

A custom-trained object detection model identifies potholes and other road defects from images and video frames. The model is optimized for road surface variations and varying lighting conditions, providing high accuracy in defect detection.
Key capabilities:
  • Detection of potholes and surface defects
  • Adaptation to different weather and lighting
  • Bounding box annotation for defects
  • Model training on cleansed, high-quality data
03

Manual Data Mapping and Annotation

To ensure high model accuracy, collected footage is manually mapped and annotated. This process allows the model to learn correct classifications, even in challenging conditions such as varying angles, shadows, or partial defects.
Key capabilities:
  • Keyframe selection from videos
  • Manual annotation of defects
  • Quality control for input data
  • Preparation of reliable training datasets
04

Prototype Output and Visualization

The system outputs annotated images or reconstructed videos with detected defects highlighted. This visualization helps engineers and maintenance teams quickly identify problem areas and plan repair work effectively.
Key capabilities:
  • Annotated images with bounding boxes
  • Reconstructed video output from processed frames
  • Highlighting multiple defect types
  • Clear visualization for decision making

Business Value

Proof of Concept Delivered: Successfully demonstrated automated detection of road defects, validating the feasibility of AI-assisted road maintenance.

Improved Model Accuracy: High-quality, manually mapped data enhanced the model's detection precision, establishing a baseline for further optimization.

Foundation for Automation: Prototype enables municipalities and automotive companies to automate defect detection, reduce maintenance costs, and support strategic planning for repairs.

Enhanced Research Capability: Provides a platform for iterative improvements in computer vision models applied to infrastructure monitoring.

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