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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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All Technologies Used

Python
Python
Caffe
Caffe
OpenCV
OpenCV

Motivation

To design and build a prototype system capable of detecting road defects, such as potholes, using machine learning and computer vision technologies.

Main Challenges

Challenge 1
Lack of High-Quality Datasets

There were no publicly available datasets with sufficient images of potholes and road defects, making model training difficult.

Challenge 2
Data Inconsistency

Variations in lighting conditions, weather, and camera angles made collected data inconsistent and required extensive manual mapping.

Challenge 3
Low-Quality Video Clips

Poor resolution or quality of clips distorted the model training process, requiring strict quality checks during data preparation.

Key Features

  • Detection of road defects, including potholes, using bounding boxes.
  • Capability to process both images and videos, splitting videos into individual frames for analysis.
  • Custom-trained object detection model optimized for road defect identification.

Our Approach

Live Data Collection
To address the challenges, the team collected live data by driving on roads and capturing footage.
Keyframe Analysis
Videos were split into keyframes, and each frame was manually mapped to identify potholes and defects.
Custom Machine Learning Model
A custom machine learning model was trained from scratch using high-quality, cleansed data.
Prototype Development
The prototype processes images or videos, outlines detected defects with bounding boxes, and outputs an annotated video or image.

Project Impact

Proof of concept: Delivered a functional prototype capable of detecting road defects with high accuracy.

Improved data quality: Demonstrated the importance of high-quality data for effective machine learning model training.

Potential for automation: Prototype provides a foundation for automating road maintenance and improving vehicle navigation systems.

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