Highway Asphalt Pavement Degradation Classification Using Deep Learning and Computer Vision

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 Port Harcourt, Nigeria Chapter.

The problem

Highway pavement degradation is a prevalent situation in Nigeria. Due to the large land mass of Nigeria and the harsh weather conditions, it becomes stressful and time-consuming to use the regular ways of onsite observation. This could also disrupt traffic on the highway.

Deep Learning and Computer Vision can be used to identify and also classify the degraded asphalt pavement using drone images all in real-time. This project aims to increase the classes and also address highways in Nigeria.

The goals

In this project, we aim to develop a Deep Learning model that will identify and classify different types of asphalt pavement degradations. The primary goal of the project is to accurately identify asphalt pavement degradation.

The goals of the project are:

• Data Collection and Exploratory Data Analysis
• Preprocessing/annotation
• Feature Extraction
• Model Development and Training
• Evaluation of the Model
• App development/Deployment

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