Road Damage Detection and Classification using YOLOv5 on an Android Device

Abstract

Abstract: Most of the rural or the urban municipalities and road authorities have hard times to map the surface damages caused due to various reasons such as heavy rains, natural calamities or other factors which lead to cracks and holes to appear on the surface of the roads. These organisations or private entities look out for solutions to implement automated methods of reporting damages on a surface of the road. In most cases, they lack the technology required for the purpose of mapping the damages on the roads. One of the biggest problems for commuters is that they have to face a lot of damaged sections of road which leads to frequent reduction of the speed at which they travel, wasting a lot of time and effort from the riders perspective, which thereby increases the travel time to their destinations. Damage to the road can be fatal many times when travelling at higher speed and all of a sudden meeting a damaged part of the road. Moreover, commuters are at more risk while driving at night time due to improper visibility of the damage to a part of the road. Artificial Intelligence and Machine Learning has the potential to make traffic more efficient, ease traffic congestion in most parts of the road network in an urban or rural area, reduce driver's time and efforts. As AI helps to keep road traffic flowing, it can also enhance the efficiency of fuel consumption to an extent which are caused by vehicles idling when stationary and improve air quality of a city and urban planning for road networks. Moreover, it has the potential to detect frequent congestion and the reason behind it and also propose a solution for the same. Most of the time these congestion are caused by damages to the road making the commuters travel at a much lesser speed than what is recommended.