Open Access
Issue
EPJ Web Conf.
Volume 343, 2025
1st International Conference on Advances and Innovations in Mechanical, Aerospace, and Civil Engineering (AIMACE-2025)
Article Number 05001
Number of page(s) 10
Section Artificial Intelligence & Machine Learning in Engineering
DOI https://doi.org/10.1051/epjconf/202534305001
Published online 19 December 2025
  1. M. Yaseen, What is yolov8: An in-depth exploration of the internal features of the next-generation object detector, arXivpreprint arXiv:2408.15857, (2024). [Google Scholar]
  2. Wang et al., Cspnet: A new backbone that can enhance learning capability of cnn, in CVPR, (2020). [Google Scholar]
  3. Y. Bernard et al., Worldwide use of remote sensing to measure motor vehicle emissions, The International Council on Clean Transportation, White Paper, Aug (2019). [Online]. Available: https://theicct.org/publication/worldwide-use-of-remote-sensing-to-measure-motor-vehicle-emissions/ [Google Scholar]
  4. H. Xie, Y. Zhang, Y. He, K. You, P. Dai, B. Fan, others, and W. Liu, On-road high-emitting vehicle identification by an automatic hyperparameter optimization model based on a remote sensing system, Measurement, 225, 113938, (2024). [Google Scholar]
  5. M. Li, Y. Tang, K. Wu, and H. Cheng, Autonomous vehicle pollution monitoring: An innovative solution for policy and environmental management, Transportation Research Part D: Transport and Environment, 139, 104542, (2025). [Google Scholar]
  6. Y. Bernard and J. German, Remote sensing of vehicle emissions: A tool to support air quality management, The International Council on Clean Transportation, Briefing Paper, Dec (2018). [Online]. Available: https://theicct.org/sites/default/files/publications/ICCTremote-sensing brief 201812.pdf [Google Scholar]
  7. J. Borken-Kleefeld and T. Dallmann, Remote sensing of motor vehicle exhaust emissions, The International Council on Clean Transportation, Tech. Rep. , (2018). [Google Scholar]
  8. O. Ghaffarpasand, K. Ropkins, D. C. S. Beddows, and F. D. Pope, Detecting high emitting vehicle subsets using emission remote sensing systems, Sci Total Environ, 858(Part 2), 159814, (2023). [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0048969722069145 [Google Scholar]
  9. M. Knoll, M. Penz, H. Juchem, C. Schmidt, D. Pöhler, and A. Bergmann, Large-scale automated emission measurement of individual vehicles with point sampling, Atmos. Meas. Tech., 17, 2481–2505, (2024). [Google Scholar]
  10. A. S. Devi, M. M. J. Britto, Z. Fang, R. Gopan, P. S. Jassal, M.M. Qazzaz, others, and F.M. Al-Sallami, Internet-of-vehicles network for $\text{CO}_2$ emission estimation and reinforcement learning-based emission reduction, IEEE Access, (2024). [Google Scholar]
  11. Roboflow, Smoke vehicles dataset, (2024), accessed: Dec. 1, 2024. [Online]. Available: https://universe.roboflow.com/alpha-ai-nwrrb/smoke-vehicles [Google Scholar]
  12. European Commission, Using remote sensing technology to measure vehicle emissions, CORDIS EU Research Results, (2023). [Online]. Available: https://cordis.europa.eu/article/id/446410-using-remote-sensing-technology-to-measure-vehicle-emissions [Google Scholar]
  13. The Real Urban Emissions Initiative (TRUE), Using remote sensing technology to measure real-world vehicle emissions, TRUE Initiative, (2024), accessed: Dec. 2, 2024. [Online]. Available: https://www.trueinitiative.org [Google Scholar]
  14. Clean Air Fund, Vehicle pollution emissions, Clean Air Fund News, (2024). [Online]. Available: https://www.cleanairfund.org/news-item/ vehicle-pollution-emissions/ [Google Scholar]
  15. Opus Group, Remote sensing for vehicle inspection, Opus Vehicle Inspection, (2024). [Online]. Available: https://www.opus.global/vehicle-inspection/ remote-sensing/ [Google Scholar]
  16. Khaleej Times, Uae: New system to measure vehicle emissions in abu dhabi using latest technology, Khaleej Times, (2024). [Online]. Available: https://www.khaleeitimes.com/uae/environment/uae-new-system-to-measure-vehicle-emissions-in-abu-dhabi-using-latest-technology [Google Scholar]
  17. G. Jocher et al., Ultralytics yolov8: A state-of-the-art object detection model, GitHub repository, (2023). [Online]. Available: https://docs.ultralytics.com/models/yolov8/ [Google Scholar]
  18. Yolov8 metrics: Unveiling key insights, (2024). [Online]. Available: https://yolov8.org/unveiling-the-secrets-of-yolov8-metrics/ [Google Scholar]
  19. How to evaluate yolov8 model: A comprehensive guide, (2024). [Online]. Available: https://yolov8.org/how-to-evaluate-yolov8-model/ [Google Scholar]
  20. Liu et al., Path aggregation network for instance segmentation, in CVPR, (2018). [Google Scholar]
  21. Lin et al., Feature pyramid networks for object detection, in CVPR, (2017). [Google Scholar]
  22. Hosang et al., Learning non-maximum suppression, in CVPR, (2017). [Google Scholar]
  23. R. Smit and P. Kingston, Measuring on-road vehicle emissions with multiple instruments including remote sensing, Atmosphere, 10(9), 516, (2019). [Google Scholar]
  24. Q. Zhang, N. Wei, C. Zou, and H. Mao, Evaluating the ammonia emission from in-use vehicles using on-road remote sensing test, Environ Pollut, 271, 116384, (2021). [Google Scholar]
  25. Z. Xu, R. Wang, R. Wang, C. Zhang, and X. Xia, Unsupervised identification of high-emitting mobile sources based on multi-feature fusion, in 2021 China Automation Congress (CAC). IEEE, 652–657, (2021). [Google Scholar]
  26. A. Rauniyar, T. Berge, A. Kuijpers, P. Litzinger, B. Peeters, E.V. Gils, others, and J.E. Håkegård, Nemo: Real-time noise and exhaust emissions monitoring for sustainable and intelligent transportation systems, IEEE Sensors J, (2023). [Google Scholar]
  27. S. M. Rahman, A systematic literature review of satellite-based monitoring systems in road transportation: Techniques, applications, and challenges, Frontiers in Applied Engineering and Technology, 1(01), 286–303, (2024). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.