TRACTS-Net : An intelligent road damage detection system using 5G integrated team-forming network

• Main Authors: Rahman, Md Arafatur, Muhammad Afiq, Azmi, Mohammad Nafees, Zaman, Naeem, Muhammad Kamran, Pillai, Prashant, Patwary, Mohammad Nuruzzaman
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/42420/1/TRACTS-Net_An%20intelligent%20road%20damage%20detection%20system%20using%205G.pdf; http://umpir.ump.edu.my/id/eprint/42420/2/TRACTS-Net_An%20intelligent%20road%20damage%20detection%20system%20using%205G%20integrated%20team-forming%20network_ABS.pdf; http://umpir.ump.edu.my/id/eprint/42420/; https://doi.org/10.1109/5GWF52925.2021.00089

In the era of fifth generation of cellular communication (5G), connected vehicles are expected to play a crucial role in transportation and road safety. Every year, road accidents cause numerous injuries and deaths all over the world. One of the various reasons for these accidents is the damaged roads. However, recent technological advancements have provided us with the opportunity to overcome these challenges and mitigate the number of accidents drastically. Thus, in this manuscript, we developed a cost-effective IoT device to capture information of potholes on the roads and alert the authority through gateways with the aid of our proposed architecture which integrates 5G networks. Experimental investigations have been carried out to test the performance of our model and our findings demonstrate that the proposed device performs significantly well in the testbed with an accuracy of little less than cent percent in team-forming network.