Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques

Abstract

The increasing number of vehicles on the road has made traffic regulations challenging to manage, particularly in large and crowded cities. Real-time traffic monitoring systems are one of the most important factors that enable efficient traffic flow and enhanced mobility. Therefore, vehicles and drivers have always needed reliable and accurate real-time traffic information. Recently, various solutions have been proposed to solve the problems and concerns in traffic situations. One alternative solution is vehicular cloud computing (VCC). Additionally, an IoT-aided robotic (IoRT) model has been developed with a modern architecture that integrates IoT sensor nodes and cameras to gather real-time traffic data. The main contributions of this research work are to implement two deep learning techniques based on modified LeNet-5 for real-time traffic sign recognition and the transfer learning-based Inception-V3 model for detecting and recognizing traffic lights. Furthermore, optimal distance was found between the ultrasonic sensors and the obstacles using ultrasonics’ waves time and speed to reduce road accidents. The data, which is collected by sensors and cameras, is processed using various image processing algorithms and it is sent to the cloud to be available for drivers and commuters through a mobile application. Test results indicate that the proposed models have significant improvements in terms of accuracy. The modified LeNet-5 achieved accuracy rates of 99.12% and 99.78% on the German Traffic Sign Recognition Benchmark (GTSRB) and extended GTSRB (EGTSRB) datasets, respectively, whereas the second model, trained on Laboratory for the Intelligent and Safe Automobiles (LISA) dataset, attained a 98.6% accuracy rate. Compared to the related traffic monitoring systems, the findings of this study outperform other works by 3.78% for traffic sign recognition and by 1.02% for traffic light detection and recognition.