AI Based Road Inspection System for Mexico

This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Mexico City, Mexico Chapter.

The problem

Current practices of performing road inspections are time-consuming and labour-intensive. Road surfaces degrade on a daily basis as a result of the heavy traffic on them. This will not only impact the driver’s comfort but will also impact economic efficiency. To maintain roads as efficiently as possible, municipalities perform regular inspections. The aim of the project is to use machine learning to study and analyze different types of road defects and to automatically detect any road abnormalities. We will design, build and test an inspection system for this purpose. The system is equipped with a camera to collect video streams from different roads with and without defects. Then, the captured data will be analyzed using the Matlab machine learning toolbox to train and test the network. Finally, the system will provide recommended actions for the municipality related to actions required to fix/correct road defects. The approach is divided into 3 main tasks: Data acquisition, Data Training/Testing, and Dashboard Building and Testing.

• Data acquisition stage: In this stage, we will use HD cameras to capture live videos of different road defects and issues. We will also collect both images from standard datasets, images from real roads, and live video recordings.

• Data Training/Testing: Collecting and labeling roads are often tedious and many times require expert knowledge. Therefore, we decided to use transfer learning to address challenges related to the scarcity of data and the lack of human labels. Matlab machine learning toolbox will be used to classify road defects. We will also use standard image processing techniques to highlight areas and guide the inspection process.
• Dashboard Building and Testing: The dashboard or the graphical interface will visualize the defects and the recommendation to the municipality.

The goals

The goal of this work is to investigate the ability of various machine learning classifiers to detect road defects with the highest possible accuracy, as well as to build a Dashboard to visualize detected road defects.

This project aims to:

• Automate the inspection process to reduce time and effort for better efficiency.
• Collect as much data as possible about UAE roads (Cracks, Patching, Rutting, and deformation).
• Apply machine learning algorithms and image processing techniques to detect various road defects.
• Classify images using a pre-trained deep convolutional neural network (e.g., GoogLeNet, SqueezeNet…etc.).
• Visualize road defects in real time using dashboards/graphical user interface.
• Reporting the machine learning approach.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work