A 2016 study done by IIT Delhi says that the National Highways comprise only 15% of the total length of roads in India but account for 33% of the fatalities. Trucks and buses are involved in about 70% of fatal crashes in both rural and urban areas.
With the number of deaths caused by road-related issues and accidents reaching 400 per day in countries like India, it has become imperative to find solutions to minimise road fatalities.
Aerial Monitoring overcomes the limitations of traditional methods of traffic data collection due to its mobility, complexity, and ability to cover large areas. Airborne sensors provide a sufficient amount of data to enable vehicle location and movement monitoring. However, further processing and evaluation of the data often require tremendous effort.
Apart from the growing use cases of on board computer vision applications, multinational construction companies like BAM Infra Nederland and OrangeNXT are considering applying AI to the other end of the problem — construction. They have used machine learning along with services like Azure to train algorithms that can accurately detect and classify various types of damage on paved surfaces.
AI In Road & Bridge Construction
BAM recommends a new solution where a vehicle equipped with 360-degree cameras records video footage from every angle. This footage is then uploaded to the Azure cloud, where AI-powered algorithms automatically skim the video for anomalies.
Even geospatial data is captured in these images, which will allow inspectors to accurately trace them back to their real-world location.
This eliminates the burden of going through long hours of footage from undamaged roads. And, the inspectors can now focus on sections which require attention, leading to quicker action.
Strategies such as these satisfy all the technical as well as the business end of problems by improving the speed, quality and accuracy of visual road checks, enabling predictive maintenance while reducing costs.
For inspecting the bridges, there can’t be a better alternative than flying a drone. These drones capture the concrete structure in many angles resulting in thousands of images.
Now the manual inspectors can just sit back and check these images for any flaws instead of risking themselves. A classic case of augmentation of machine intelligence, where the domain expertise of this personnel can now be used to help train a machine learning algorithm, which can automatically detect cracks in the surface of the concrete by checking the photos uploaded to cloud.
Embarking On A Safer Journey
The success of these novel inspection methods by will encourage more companies to consider building their own solutions or to buy one as a software-as-a-service solution in other countries, opening up a new business opportunity while ensuring that they remain competitive.
Developed countries have already embarked on a non-human technological journey to alleviate fatal human flaws, however developing economies like India, still have to catch up. With the advent of drone technologies and the improved accessibility and awareness of algorithmic driven solutions, India doesn’t have to wait for roads or the people to get better.
In India, the Ministry of Civil Aviation has been working for several years to establish a world-leading drone ecosystem in India. There are also well framed regulations in place that would keep the foul players in check while guaranteeing the incessant inflow of technological advancements.
Along with aerial monitoring, there can be other methods such as the one in development by the civil engineering department at IIT Hyderabad where they have developed a model that would help build roads in a smarter way by accurately predicting their performance.
From identifying cracks in the concrete to applying AI during the construction phase itself proves the wide range of applications that are possible. Not only that but having drones fly around for inspection also reduces the risks associated with manual inspection. Civil engineering is now ripe for a new field of AI oriented job generation that can assist the preexisting departments.
