K. Krishna Jyothi,
D Chandravathi,
- Assistant Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Andhra University, Telangana, India
- Associate Professor, CSE Department GVP College for Degree and PG Courses(A) Andhra University, Andhra Pradesh, India
Abstract
Introducing a revolutionary paradigm in road safety, our project unveils the Intelligent Traffic Surveillance System (ITSS), a groundbreaking initiative poised to transform urban traffic management. In an era where road safety is paramount, ITSS emerges as a beacon of innovation, harnessing the prowess of computer vision and machine learning to tackle two of the most pressing concerns plaguing our roads: helmet non-compliance and triple riding among motorcyclists. At its core, ITSS leverages state-of-the-art technology, notably the YOLO (You Only Look Once) machine learning model, to deliver unparalleled real-time detection capabilities. With lightning-fast precision, ITSS identifies instances of helmet violations and triple riding, revolutionizing the landscape of traffic surveillance. Through seamless integration with existing infrastructure, relevant data is swiftly relayed to authorities, empowering enforcement efforts and fostering a culture of compliance. ITSS is more than just a surveillance system; it’s a catalyst for change. By automating surveillance processes and minimizing response times to violations, ITSS not only enhances road safety but also optimizes traffic flow, ushering in a new era of efficiency on our streets. Our vision is clear: to modernize traffic management through intelligent, data-driven solutions, forging safer, smarter communities for generations to come
Keywords: Intelligent Traffic Surveillance System, YOLO, Convolutional Neural Networks, YOLOv8
[This article belongs to International Journal of Robotics and Automation in Mechanics (ijram)]
K. Krishna Jyothi, D Chandravathi. Revolutionizing Motorcycle Safety: A Deep Learning Approach for Helmet and Triple Riding Detection using Computer Vision Technology and Machine Learning Model. International Journal of Robotics and Automation in Mechanics. 2024; 02(01):28-36.
K. Krishna Jyothi, D Chandravathi. Revolutionizing Motorcycle Safety: A Deep Learning Approach for Helmet and Triple Riding Detection using Computer Vision Technology and Machine Learning Model. International Journal of Robotics and Automation in Mechanics. 2024; 02(01):28-36. Available from: https://journals.stmjournals.com/ijram/article=2024/view=171238
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| Volume | 02 |
| Issue | 01 |
| Received | May 21, 2024 |
| Accepted | June 5, 2024 |
| Published | September 9, 2024 |
