Smart City Gnosys
Smart city article details
| Title | Deep Learning-Driven Smart Signal Systems For Advanced Image And Video Processing In Urban Infrastructure |
|---|---|
| ID_Doc | 17980 |
| Authors | Bharaty K.S.; Konduri P.S.R. |
| Year | 2025 |
| Published | Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025 |
| DOI | http://dx.doi.org/10.1109/STCR62650.2025.11019849 |
| Abstract | The increasing integration of intelligent systems in metropolitan environments emphasizes the important role effective image and video processing performs in applications including the traffic management, the monitoring of safety, and the urban planning. When handling dynamic scenarios, that is, changes in traffic conditions, occlusions, and the need for real-time analysis, conventional signal processing methods sometimes fail. Strong and adaptable solutions able to manage both these complexity as well as others are thus much needed. This work intends to introduce a deep learning-based smart signal system designed to process real-time video feeds for uses connected to urban traffic and safety. The proposed system uses recurrent neural networks (RNNs) for temporal sequence comprehension; conversely, convolutional neural networks (CNNs) are used for spatial data analysis. Two approaches used in order to improve the performance of the model are data augmentation and transfer learning, which increase the accuracy of decision-making using multi-modal data sources, video streams, sensor inputs, environmental parameters. This helps one to get beyond the challenge of small datasets. System performance is discovered using benchmark datasets and video from actual traffic events. Better than the methods now in use, the results show object detection has 95% accuracy and anomaly detection has 92% accuracy. Promising for use in smart cities, the proposed framework exhibits scalability and flexibility over a wide range of events. Its characteristics, traffic optimization, safety monitoring, and data-driven urban planning, which comprise traffic control, safety monitoring, and data-driven urban planning, have the ability to drastically change urban transportation infrastructure management. © 2025 IEEE. |
| Author Keywords | Deep learning; image processing; smart signal systems; urban infrastructure; video processing |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
51592  | 0.921 | Pritha A.; Fathima G. | Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review | IET Conference Proceedings, 2024, 23 (2024) |
| 32606 | 0.916 | Kumar A.; Ranjan R. | Intelligent Traffic Identification System Powered Byconvolutional Neural Networks | ACM International Conference Proceeding Series (2023) |
| 52587 | 0.916 | Priya K.; Omkar K.; Naresh R.; Venkatesh K. | Spatio-Temporal Traffic Prediction In Urban Area Using Convolutional Temporal Network System | 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings (2025) |
| 44480 | 0.913 | Srikanth M.; Krishna N.S.V.S.S.J.; Krishna S.J.S.; Irfan S.; Venkat T.G. | Real-Time Vehicle Detection And Road Condition Prediction For Smart Urban Areas | Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024 (2024) |
| 60735 | 0.912 | Kumari M.; Ulmas Z.; Suseendra R.; Ramesh J.V.N.; El-Ebiary Y.A.B., Prof. | Utilizing Federated Learning For Enhanced Real-Time Traffic Prediction In Smart Urban Environments | International Journal of Advanced Computer Science and Applications, 15, 2 (2024) |
| 58647 | 0.905 | Ismaeel A.G.; Janardhanan K.; Sankar M.; Natarajan Y.; Mahmood S.N.; Alani S.; Shather A.H. | Traffic Pattern Classification In Smart Cities Using Deep Recurrent Neural Network | Sustainability (Switzerland), 15, 19 (2023) |
| 1395 | 0.903 | Tripathi A.N.; Sharma B. | A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning | Lecture Notes in Mechanical Engineering (2023) |
| 44467 | 0.903 | Saranya V.S.; Subbarao G.; Balakotaiah D.; Bhavsingh M.; Babu K.S.; Dhanikonda S.R. | Real-Time Traffic Flow Optimization Using Adaptive Iot And Data Analytics: A Novel Deepstreamnet Model | 4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings (2024) |
| 6363 | 0.902 | Mok K.; Zhang L. | Adaptive Traffic Signal Management Method Combining Deep Learning And Simulation | Multimedia Tools and Applications, 83, 5 (2024) |
| 40920 | 0.9 | Abdullah S.M.; Periyasamy M.; Kamaludeen N.A.; Towfek S.K.; Marappan R.; Kidambi Raju S.; Alharbi A.H.; Khafaga D.S. | Optimizing Traffic Flow In Smart Cities: Soft Gru-Based Recurrent Neural Networks For Enhanced Congestion Prediction Using Deep Learning | Sustainability (Switzerland), 15, 7 (2023) |