Researchers in India and Japan are developing Smartphone-based mapping of road health. A cheap and easy-to-use technology for monitoring road conditions is the goal of the collaborative effort. Its goal is to lessen the number of collisions resulting from deteriorated road conditions. University of Tokyo scientists have collected almost 31,000 Japanese roadways photos showing signs of corrosion or fissures. Olomouc, Prague, and Bratislava in the Czech Republic have also tested a similar approach.
Key Highlight:
- Researchers in India and Japan are developing Smartphone-based mapping of road health.
- A cheap and easy-to-use technology for monitoring road conditions is the goal of the collaborative effort.
- University of Tokyo scientists have collected almost 31,000 Japanese roadways photos showing signs of corrosion or fissures.
- Olomouc, Prague, and Bratislava in the Czech Republic have also tested a similar approach.
- The goal is to lessen the number of collisions resulting from deteriorated road conditions.
- The same system has been used to survey roads in the Olomuco, Prague and Bratslav regions of Czech Republic.
- The results could be used to devise better maintenance plans.
Researchers in both countries are developing Smartphone-based mapping of road health in India and Japan to reduce accidents caused by deteriorated roads.
India’s Indian Institute of Technology, Roorkee and the University of Tokyo are working together to develop a cheap and deployable system for automated road condition monitoring, which will lead to increased road safety.
Alexander Mraz, a data scientist from Luxembourg, also contributes to the team’s work.
According to road safety experts, road surface abnormalities, such as potholes, cracks and bumps, impair driving comfort and on-road safety. Detecting these anomalies is the key to road condition monitoring.
An IIT-Roorkee professor overseeing the Indian team’s research says that road infrastructure is key for providing essential transportation services to people and commodities worldwide because of its role in facilitating economic growth and development.
Roads must be maintained routinely and thoroughly inspected at least once a year because the condition of the roads directly and significantly affects traffic safety.” When assessing road conditions, “hand inspection of the road surface is required,” she said.
Due to the large area of road networks that must be evaluated in a short period of time, these methods are no longer adequate.” She said that many local governments cannot carry out the necessary inspections on time because of a lack of funds.
Deep-learning-based algorithms that can automatically identify and classify road damage using smartphone photographs were developed with the Sekimoto Laboratory at the University of Tokyo, Japan.
In order to construct a smartphone-based application that can assess road conditions at any time, anyplace, this study sets the stage.” It is possible to capture road damage on smartphones and upload immediately to cloud servers using this app. ” As a result, it is possible to utilize the app to monitor and assess high temporal fidelity road damage continuously. By posting photographs of any road damage they see, the general people may help. Using this information, road agencies may devise better maintenance plans. Dikhsa Arya, the primary researcher from India on the project, claimed that the successful implementation would save time and money and provide safer roadways for everyone.
More than 31,000 pictures of roads with identified cracks and defects have been mapped so far by the team’s researchers. Ichihara, Chiba, Sumida, Nagakute, Adachi, Muroran, and Numazu are all included in the survey area of Japan.
The team has conducted surveys in Delhi, Gurgaon, and Haryana in India. For example, the same system has been used in the Czech Republic to survey roads in the Olomouc, Prague, and Bratislava regions.
Alligator cracks and potholes are among the four categories of road damage included in the dataset, which is designed for developing deep learning-based algorithms for automatically detecting and classifying road damage.” It’s useful for towns and road agencies to create low-cost monitoring methods for road pavement surface problems,” said Hiroya Maeda from the University of Tokyo. The photographs were acquired using vehicle-mounted smartphones.
Machine learning researchers might use the datasets to compare the performance of different algorithms in handling similar challenges, Maeda added.
According to a “Road Accidents in India” report from the Ministry of Road Transport & Highways (MoRTH), more accidents occur when drivers have to deal with roads with sharp turns, potholes, and steep gradients.
Stats published in India’s parliament show that the overall number of road accidents caused by potholes in 2016 was 6,424, 9,423, 4,869 in 2020, and 4,775 in 2019.
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