Smart City Gnosys
Smart city article details
| Title | Cogniguardianroadscape: Advancing Safety Through Ai-Driven Roadway Audits |
|---|---|
| ID_Doc | 14630 |
| Authors | Thangaraju S.; Nagarajan M.; Ganesan M.; Raja S.; Sirotiya A.; Jasrotia B. |
| Year | 2025 |
| Published | SAE Technical Papers |
| DOI | http://dx.doi.org/10.4271/2025-28-0019 |
| Abstract | The escalation of road infrastructure anomalies, such as speed breakers and potholes, presents a formidable challenge to vehicular safety, efficient traffic management, and road maintenance strategies worldwide. In addressing these pervasive issues, this paper proposes an advanced, integrated approach for the detection and classification of speed breakers and potholes. Utilizing a sophisticated blend of deep learning methodologies and enhanced image processing techniques, our solution leverages Object Detection to analyze and interpret real-time visual data captured through advanced vehicle-mounted camera systems. This research meticulously details the comprehensive process involved in the development of this system, including the acquisition and preprocessing of a vast, varied dataset representative of numerous road types, conditions, and environmental factors. Through rigorous training, testing, and validation phases, the model demonstrates remarkable proficiency in recognizing and classifying road imperfections, outperforming existing models in accuracy, precision, recall, and F1 score metrics. The innovative approach underscores the potential of integrating cutting-edge machine learning algorithms with real-world applications to significantly enhance autonomous navigation systems and assist in the proactive maintenance of road infrastructure. Furthermore, the paper discusses the model's scalability and adaptability, emphasizing its potential application across a wide range of environments, weather conditions, and vehicular technologies. By providing a detailed analysis of the system's performance under varied operational scenarios, this research contributes valuable insights into the realm of intelligent transportation systems and smart city infrastructure development, the findings of this study not only pave the way for future advancements in autonomous vehicle technologies but also offer promising perspectives for the improvement of road maintenance and safety protocols. Looking ahead, the paper outlines a series of potential enhancements and areas for future research, including the optimization of the detection algorithm for increased efficiency and the exploration of its integration with other sensory data sources for a more holistic approach to road anomaly detection. © 2025 SAE International. All Rights Reserved. |
| Author Keywords | Pothole detection; real-time data analysis; road safety and maintenance; speed breaker identify image processing techniques |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
31709  | 0.905 | Awan A.Z.; Ji J.C.; Uzair M.; Ullah I.; Riaz W.; Gong T. | Innovative Road Distress Detection (Ir-Dd): An Efficient And Scalable Deep Learning Approach | PeerJ Computer Science, 10 (2024) |
| 15438 | 0.898 | Assemlali H.; Bouhsissin S.; Sael N. | Computer Vision-Based Detection And Classification Of Road Obstacles: Systematic Literature Review | IEEE Access (2025) |
| 44425 | 0.892 | Bhosale S.B.; Ponnusamy S. | Real-Time Pothole Detection Using Yolov7: An Efficient Deep Learning Approach For Road Safety And Maintenance | 2025 International Conference on Data Science and Business Systems, ICDSBS 2025 (2025) |
| 30906 | 0.885 | Merolla D.; Latorre V.; Salis A.; Boanelli G. | Improving Road Safety With Ai: Automated Detection Of Signs And Surface Damage | Computers, 14, 3 (2025) |
| 16106 | 0.884 | Lakshminarayanan S.; Konidhala J. | Convolutional Neural Network For Pothole Identification In Urban Roads | International Journal Of Advances In Signal And Image Sciences, 10, 1 (2024) |
| 44435 | 0.88 | Kulambayev B.; Gleb B.; Katayev N.; Menglibay I.; Momynkulov Z. | Real-Time Road Damage Detection System On Deep Learning Based Image Analysis | International Journal of Advanced Computer Science and Applications, 15, 9 (2024) |
| 44480 | 0.875 | 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) |
| 7037 | 0.872 | Khedr M.A.; Abdelaziz M. | Ai-Driven Robotic System For Predictive Maintenance: Urban Road Defect Detection In Smart Cities | 2024 International Conference on Computer and Applications, ICCA 2024 (2024) |
| 42511 | 0.872 | Sai K.K.; Kumar D.D.V.; Sahrudhay A.; Dharavath K. | Pothole Detection Using Deep Learning | 2023 2nd International Conference on Futuristic Technologies, INCOFT 2023 (2023) |
| 204 | 0.869 | Alshammari S.; Song S. | 3Pod: Federated Learning-Based 3 Dimensional Pothole Detection For Smart Transportation | ISC2 2022 - 8th IEEE International Smart Cities Conference (2022) |