International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Lane Detection Network: Using Artificial Intelligence and Deep Learning

Abstract

Lane detection, a pivotal technology in autonomous vehicles, involves the identification and tracking of lane markings on roadways using computer vision or image processing techniques. In this study, we utilize the Yolov8 semantic segmentation model to train our dataset. Our objective is to implement this solution road, with customized data sourced from different roads and highways. The model demonstrates exceptional accuracy, rendering it highly dependable for segmentation tasks. Even across a broader Intersection over Union (IoU) range (0.5:0.9), the model maintains a commendable mAP of 0.842.

Country : India

1 Divyanka Thakur2 Priya Pal3 Amogh Jadhav4 Numaira Kable5 Radhika Nanda

  1. Department of Computer Science Engineering (AI&ML), Smt. Indira Gandhi College of Engineering, Navi Mumbai, India
  2. Department of Computer Science Engineering (AI&ML), Smt. Indira Gandhi College of Engineering, Navi Mumbai, India
  3. Department of Computer Science Engineering (AI&ML), Smt. Indira Gandhi College of Engineering, Navi Mumbai, India
  4. Department of Computer Science Engineering (AI&ML), Smt. Indira Gandhi College of Engineering, Navi Mumbai, India
  5. Department of Computer Science Engineering (AI&ML), Smt. Indira Gandhi College of Engineering, Navi Mumbai, India

IRJIET, Volume 8, Issue 3, March 2024 pp. 328-331

doi.org/10.47001/IRJIET/2024.803050

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References

  1. Al Mamun A, Ping EP, Hossen MJ. “A Deep Learning Instance Segmentation Approach for Lane Marking Detection”. Int. J. Com. Dig. Sys. 2022 Sep; 12(1).
  2. Cheng, Wangfeng & Wang, Xuanyao & Mao, Bangguo. (2023). Research on Lane Line Detection Algorithms Based on Instance Segmentation. Sensors. 23. 789. 10.3390/s23020789.
  3. X. Zhang, W. Yang, X. Tang, and J. Liu, “A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3” Sensors, vol. 18, no. 12, p. 4308, Dec. 2018, doi: 10.3390/s18124308.
  4. Y. Ko, Y. Lee, S. Azam, F. Munir, M. Jeon and W. Pedrycz, "Key Points Estimation and Point Instance Segmentation Approach for Lane Detection," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 8949-8958, July 2022, doi: 10.1109/TITS.2021.3088488.
  5. Ju Han Yoo, Student Member, IEEE, Seong-Whan Lee, Fellow, IEEE, Sung-Kee Park, Member, IEEE, and Dong Hwan Kim, Member, IEEE March 2017 IEEE Transactions on Intelligent Transportation Systems, PP(99):1-13 DOI:10.1109/TITS.2017.2679222.
  6. Zhao et al., "YOLO-Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight," Mathematical Problems in Engineering, 2021.
  7. Dawam et al., "Smart City Lane Detection for Autonomous Vehicle," IEEE Intl Conf on Dependable, Autonomic and Secure Computing, 2020.
  8. Liu et al., "Research on Lane Line Segmentation Algorithm Based on Deeplabv3," IEEE Asia-Pacific Conference on Image Processing, 2021.
  9. Yueen et al., "Vision-Based Lane Detection and Lane-Marking Model Inference: A Three-Step Deep Learning Approach," International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), 2018.
  10. Hu et al., "Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks," Advances in Multimedia, 2022.
  11. Madan et al., "Road Lane Line Detection employing OPENCV."

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