EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 16 (2025) Int. J. Simul. Multidisci. Des. Optim., 16 (2025) 6 References
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 16, 2025
Multi-modal Information Learning and Analytics on Cross-Media Data Integration
Article Number 6
Number of page(s) 15
DOI https://doi.org/10.1051/smdo/2025004
Published online 09 April 2025
  1. K. Guo, X. Li, M. Zhang, Q. Bao, M. Yang, Real-time vehicle object detection method based on multi-scale feature fusion, IEEE Access 9, 115126–115134 (2021) [CrossRef] [Google Scholar]
  2. L. Huang, C. Chen, J. Yun, Y. Sun, J. Tian, Z. Hao, H. Ma, Multi-scale feature fusion convolutional neural network for indoor small target detection, Front. Neurorobot. 16, 881021 (2022) [CrossRef] [Google Scholar]
  3. S. Tan, Z. Duan, L. Pu, Multi-scale object detection in UAV images based on adaptive feature fusion, Plos one 19, e0300120 (2024) [CrossRef] [Google Scholar]
  4. M. Qiu, L. Huang, B.H. Tang, ASFF-YOLOv5: Multielement detection method for road traffic in UAV images based on multi-scale feature fusion, Remote Sens. 14, 3498 (2022) [CrossRef] [Google Scholar]
  5. R. Shang, J. Zhang, L. Jiao, Y. Li, N. Marturi, R. Stolkin, Multi-scale adaptive feature fusion network for semantic segmentation in remote sensing images, Remote Sens. 12, 872 (2020) [CrossRef] [Google Scholar]
  6. J. Zhang, MASFF: multi-scale adaptive spatial feature fusion method for vehicle recognition, J. Comput. 33, 001–011 (2022) [CrossRef] [MathSciNet] [Google Scholar]
  7. X. Shen, H. Li, Y. Huang, Y. Wang, Vehicle detection method based on adaptive multi-scale feature fusion network, J. Electr. Imag. 31, 043008 (2022) [Google Scholar]
  8. A. Li, S. Sun, Z. Zhang, M. Feng, C. Wu, W. Li, A multi-scale traffic object detection algorithm for road scenes based on improved YOLOv5, Electronics 12, 878 (2023) [CrossRef] [Google Scholar]
  9. J. Wu, G. Dai, W. Zhou, X. Zhu, Z. Wang, Multi-scale feature fusion with attention mechanism for crowded road object detection, J. Real-Time Image Process. 21, 29 (2024) [CrossRef] [Google Scholar]
  10. Y. Zhang, L. Zhang, Y. Wang, W. Xu, AGF-Net: adaptive global feature fusion network for road extraction from remote-sensing images, Complex Intell. Syst. 10, 1–18 (2024) [CrossRef] [Google Scholar]
  11. J. Dong, Y. Wang, Y. Yang, M. Yang, J. Chen, MCDNet: multi-level cloud detection network for remote sensing images based on dual-perspective change-guided and multi-scale feature fusion, Int. J. Appl. Earth Observ. Geoinform. 129, 103820 (2024) [CrossRef] [Google Scholar]
  12. Y. Zhang, L. Li, C. Chun, Y. Wen, G. Xu, Multi-scale feature adaptive fusion model for real-time detection in complex citrus orchard environments, Comput. Electr. Agric. 219, 108836 (2024) [CrossRef] [Google Scholar]
  13. R. Hasegawa, Y. Iwamoto, Y.W. Chen, Robust Japanese road sign detection and recognition in complex scenes using convolutional neural networks, J. Image Graph. 8, 59–66 (2020) [CrossRef] [Google Scholar]
  14. Z. Shao, Z. Zhou, X. Huang, Y. Zhang, MRENet: simultaneous road surface and road centerline extraction in complex urban scenes from very high-resolution images, Remote Sens. 13, 239 (2021) [CrossRef] [Google Scholar]
  15. L. Shen, H. Tao, Y. Ni, Y. Wang, V. Stojanovic, Improved YOLOv3 model with feature map cropping for multi-scale road object detection, Measur. Sci. Technol. 34, 045406 (2023) [CrossRef] [Google Scholar]
  16. T.Y. Chow, K.H. Lee, K.L. Chan, Detection of targets in road scene images enhanced using conditional GAN-based dehazing model, Appl. Sci. 13, 5326 (2023) [CrossRef] [Google Scholar]
  17. F. Yang, Y. Rao, Vision-based intelligent vehicle road recognition and obstacle detection method, Int. J. Pattern Recogn. Artif. Intell. 34, 2050020 (2020) [CrossRef] [Google Scholar]
  18. R. Yousri, M.A. Elattar, M.S. Darweesh, A deep learning-based benchmarking framework for lane segmentation in the complex and dynamic road scenes, IEEE Access 9, 117565–117580 (2021) [CrossRef] [Google Scholar]
  19. T. Deng, X. Liu, L. Wang, Occluded vehicle detection via multi-scale hybrid attention mechanism in the road scene, Electronics 11, 2709 (2022) [CrossRef] [Google Scholar]
  20. L. Tang, L. Yun, Z. Chen, F. Cheng, HRYNet: a highly robust YOLO network for complex road traffic object detection, Sensors 24, 642 (2024) [CrossRef] [Google Scholar]
  21. Z. Zhu, X. Li, J. Zhai, H. Hu, PODB: A learning-based polarimetric object detection benchmark for road scenes in adverse weather conditions, Inform. Fusion 108, 102385 (2024) [CrossRef] [Google Scholar]
  22. A.V. Malawade, S.Y. Yu, B. Hsu, H. Kaeley, A. Karra, M.A. Al Faruque, Roadscene2vec: a tool for extracting and embedding road scene-graphs, Knowl. Based Syst. 242, 108245 (2022) [CrossRef] [Google Scholar]
  23. https://www.kaggle.com/datasets/boukraailyesali/traffic-road-object-detection-dataset-using-yolo [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.