Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques
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 dri...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Kuwait Journal of Science |
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| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410823001943 |
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| author | Mohammed Qader Kheder Aree Ali Mohammed |
| author_facet | Mohammed Qader Kheder Aree Ali Mohammed |
| author_sort | Mohammed Qader Kheder |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c852571979044dc4b0193bfb5a635512 |
| institution | DOAJ |
| issn | 2307-4116 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-c852571979044dc4b0193bfb5a6355122025-08-20T03:19:57ZengElsevierKuwait Journal of Science2307-41162024-01-01511100153https://doi.org/10.1016/j.kjs.2023.10.017Real-time traffic monitoring system using IoT-aided robotics and deep learning techniquesMohammed Qader Kheder0Aree Ali Mohammed1Dept. of Computer, College of Science, University of Sulaimani, Sulaimani, IraqDept. of Computer, College of Science, University of Sulaimani, Sulaimani, Iraq; Dept. of Computer, College of Science, University of Halabja, Halabja, IraqThe 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.https://www.sciencedirect.com/science/article/pii/S2307410823001943iot sensorsreal-time road surveillanceroad data sharingcloud databasesautonomous robotic car |
| spellingShingle | Mohammed Qader Kheder Aree Ali Mohammed Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques Kuwait Journal of Science iot sensors real-time road surveillance road data sharing cloud databases autonomous robotic car |
| title | Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques |
| title_full | Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques |
| title_fullStr | Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques |
| title_full_unstemmed | Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques |
| title_short | Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques |
| title_sort | real time traffic monitoring system using iot aided robotics and deep learning techniques |
| topic | iot sensors real-time road surveillance road data sharing cloud databases autonomous robotic car |
| url | https://www.sciencedirect.com/science/article/pii/S2307410823001943 |
| work_keys_str_mv | AT mohammedqaderkheder realtimetrafficmonitoringsystemusingiotaidedroboticsanddeeplearningtechniques AT areealimohammed realtimetrafficmonitoringsystemusingiotaidedroboticsanddeeplearningtechniques |