Moorthy Agoramoorthy
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Moorthy Agoramoorthy has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
A. Shri Vindhya
Department of Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, India
1 shared publicationD. Vinoth
Department of Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, India
1 shared publication- G. Gangadevi
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
1 shared publication - Justindhas Y1 shared publication
- Kathiravan M
Department of Computer science and Engineering ,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
1 shared publication L. Karthikeyan
Department of Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, India
1 shared publication
Latest Publications
- 1 October 2025
- Published by Springer Nature in Peer-to-Peer Networking and Applications
- Vol. 18 (6) , 1-15
A novel blockchain-integrated IoT framework for secure and efficient vehicle-to-infrastructure communication in smart transportation
- 1 October 2025
- Published by Springer Nature in Peer-to-Peer Networking and Applications
- Vol. 18 (6) , 1-15
A novel blockchain-integrated IoT framework for secure and efficient vehicle-to-infrastructure communication in smart transportation
- Open Access
- 1 June 2025
- Published by Iskender AKKURT in International Journal of Computational and Experimental Science and Engineering
- Vol. 11 (3)
A Novel Deep Learning Approach for Enhanced Roadway Pothole Detection Using YOLOv8 Instance Segmentation Algorithms
Potholes pose a major risk to roadway safety and vehicle durability, necessitating timely detection and repair. With advancements in artificial intelligence, deep learning has become crucial for automating pothole detection and segmentation. Previous studies using CNN and Haar cascade methods have achieved accuracies up to 98.2%. This paper presents a novel approach leveraging the YOLOv8 architecture for instance segmentation, enhancing detection accuracy by capturing contextual and spatial relationships. The process involves data collection, annotation, preprocessing, and model training using datasets from Roboflow Universe and Kaggle. The model's performance is assessed through sensitivity, precision, recall, F1 score, and mean average precision (mAP). Experimental results indicate a significant improvement, achieving a 99.2% mAP in pothole detection and segmentation. These findings highlight the potential of YOLOv8 in advancing automated road maintenance, ensuring safer and more efficient transportation systems.
- 1 June 2025
- Published by Iskender AKKURT in International Journal of Computational and Experimental Science and Engineering
- Vol. 11 (3)
Open AccessA Novel Deep Learning Approach for Enhanced Roadway Pothole Detection Using YOLOv8 Instance Segmentation Algorithms
Potholes pose a major risk to roadway safety and vehicle durability, necessitating timely detection and repair. With advancements in artificial intelligence, deep learning has become crucial for automating pothole detection and segmentation. Previous studies using CNN and Haar cascade methods have achieved accuracies up to 98.2%. This paper presents a novel approach leveraging the YOLOv8 architecture for instance segmentation, enhancing detection accuracy by capturing contextual and spatial relationships. The process involves data collection, annotation, preprocessing, and model training using datasets from Roboflow Universe and Kaggle. The model's performance is assessed through sensitivity, precision, recall, F1 score, and mean average precision (mAP). Experimental results indicate a significant improvement, achieving a 99.2% mAP in pothole detection and segmentation. These findings highlight the potential of YOLOv8 in advancing automated road maintenance, ensuring safer and more efficient transportation systems.