Smartphone based Deep Learning Technology is Solving India’s Biggest problem
Potholes are a big threat to road safety:
According to the statistics shared in the Indian parliament, the total number of road accidents due to potholes in 2016, 2017, 2018, 2019 and 2020 stood at 6,424, 9,423, 4,775 and 4,869 respectively.
“Road Accidents in India” report by the Ministry of Road Transport and Highways (MoRTH), roads with sharp curves, potholes and steep gradients tend to be more accident-prone because successfully negotiating them requires skill, extra care and alertness.
India-Japan collaborate to solve this problem:
Researchers from India and Japan are working on the smartphone-based mapping of road health in both the countries to reduce the accidents caused due to damaged roads. The joint project by the Indian Institute of Technology (IIT), Roorkee and the University of Tokyo is aimed at developing an affordable and deployable solution for automating the monitoring of road conditions and ultimately enhancing road safety.
How Deep Learning is solving this problem?
The team also has inputs from Alexander Mraz, a data scientist from Luxembourg in Europe. According to road safety experts, the key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks and bumps, which affect driving comfort and on-road safety. IIT-Roorkee professor Durga Toshniwal, who is supervising the Indian team in the ongoing research, said the road infrastructure holds critical socio economic importance for providing vital transportation services to people and commodities worldwide.
The research team collaborated with Sekimoto Laboratory at University of Tokyo, Japan to work on deep-learning-based algorithms which can automatically detect and classify road damage using smartphone images.
“The work lays down the foundation for building a smartphone-based application to assess road conditions, anytime, anywhere. With this app, any citizen can record the road damage with his or her smartphone and upload directly to the cloud servers.
“The dataset captures four types of road damage: longitudinal cracks, transverse cracks, alligator cracks and potholes and is intended for developing deep learning-based methods to detect and classify road damage automatically. The images were captured using vehicle-mounted smartphones, making it useful for municipalities and road agencies to develop methods for low-cost monitoring of road pavement surface conditions,” said Hiroya Maeda from the University of Tokyo.
“Further, the machine learning researchers can use the datasets for benchmarking the performance of different algorithms for solving other problems of the same type,” Maeda said.
Conclusion:
Technologies like Deep Learning and AI are now be able to tackle problems that are worrying governments and public since ages with the ease of monitoring and real time classification of damages on the roads. Collaborations like these will help the governments to work on shared expertise and curb the problem with more efficiency.
