Detection Of Lane And Speed Breaker Using Machine Learning Algorithm

  • Unique Paper ID: 164908
  • Volume: 10
  • Issue: 12
  • PageNo: 1751-1754
  • Abstract:
  • With the rapid advancement of autonomous vehicle technologies, ensuring the safety of these vehicles on roads has become a paramount concern. One of the critical aspects of safe autonomous driving is the accurate detection of lanes and potential road hazards, such as speed breakers. In this study, we propose a Lane and Speed Breaker Warning System (LSBWS) that employs machine learning algorithms to enhance the perception capabilities of autonomous vehicles. The LSBWS utilizes a combination of computer vision and machine learning techniques to detect and analyse road lanes and speed breakers in real-time. The system utilizes a camera sensor to capture the road scene ahead and then employs image processing algorithms to identify lane markings and speed breakers. A convolutional neural network (CNN) is employed to accurately detect and classify these features within the captured images. Keywords: Lane detection, Speed breaker detection, Autonomous vehicles, Machine learning algorithms, Convolutional neural network, Road safety

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{164908,
        author = {Mayur Uddhav Patil and PROF. V.S.MAHAJAN and NIRAJ PRADIP PATL and MANISH RAGHUNATH PATIL and YASH CHETAN PATIL},
        title = {Detection Of Lane And Speed Breaker Using Machine  Learning Algorithm  },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1751-1754},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164908},
        abstract = {With the rapid advancement of autonomous vehicle technologies, ensuring the safety of these vehicles on roads has become a paramount concern. One of the critical aspects of safe autonomous driving is the accurate detection of lanes and potential road hazards, such as speed breakers. In this study, we propose a Lane and Speed Breaker Warning System (LSBWS) that employs machine learning algorithms to enhance the perception capabilities of autonomous vehicles. The LSBWS utilizes a combination of computer vision and machine learning techniques to detect and analyse road lanes and speed breakers in real-time. The system utilizes a camera sensor to capture the road scene ahead and then employs image processing algorithms to identify lane markings and speed breakers. A convolutional neural network (CNN) is employed to accurately detect and classify these features within the captured images. Keywords: Lane detection, Speed breaker detection, Autonomous vehicles, Machine learning algorithms, Convolutional neural network, Road safety  },
        keywords = {Autonomous Vehicles, Lane Detection, Speed Breaker Warning, Machine Learning, Convolutional Neural Network, Computer Vision, Road Safety.  },
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 12
  • PageNo: 1751-1754

Detection Of Lane And Speed Breaker Using Machine Learning Algorithm

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