Asphalt Pavement Degradation is a common problem on Nigerian highways. Major roads linking streets and even big cities face a serious challenge of bad roads due to the state of the road. Professionals in the field have agreed that the best way to reconstruct a road is to first know the type of degradation which provides data for the best decision to take. If this is ignored, fixing a degraded highway becomes blind and less effective.
The regular way of identifying a degraded pavement will be for Engineers to do onsite surveys. The different types of pavement degradation include Linear Cracks, Crocodile Cracks, Potholes (most prevalent in Nigeria), Fatigue Cracks, Blowouts, Reflection Cracks, sinkholes, Block Cracks, Rutting, and Ravelling. This process stands a chance of being automated.
Highway pavement degradation is a prevalent situation in Nigeria. The regular operation will be for Engineers or observation onsite. Due to the large land mass of Nigeria and the harsh weather condition, it becomes stressful and time-consuming to use the regular way of onsite observation. This could also disrupt traffic on the highway.
Deep learning and computer vision could be used to identify and also classify this degraded asphalt pavement using drone images all in real-time.
Though much research has tried to build models, this project will try to increase the classes and also address Highways in Nigeria.
Data Understanding/Literature Review
Selection of technologies and stacks
Deep learning, image processing, computer vision, team work, problem solving