An Efficient Small Traffic Sign Detection Method Based on YOLOv3
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2020/11/20
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Indian UGC (journal)
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Refrences29
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Citations20
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Jixiang Wan ORCID (unauthenticated)
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Wei Ding
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Hanlin Zhu
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Ming Xia
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Zunkai Huang
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Li Tian
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Yongxin Zhu
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Hui Wang
Cite
Wan, Jixiang, et al. “An Efficient Small Traffic Sign Detection Method Based on YOLOv3”. Journal of Signal Processing Systems, vol. 93, no. 8, 2020, pp. 899-11, https://doi.org/10.1007/s11265-020-01614-2.
Wan, J., Ding, W., Zhu, H., Xia, M., Huang, Z., Tian, L., Zhu, Y., & Wang, H. (2020). An Efficient Small Traffic Sign Detection Method Based on YOLOv3. Journal of Signal Processing Systems, 93(8), 899-911. https://doi.org/10.1007/s11265-020-01614-2
Wan, Jixiang, Wei Ding, Hanlin Zhu, Ming Xia, Zunkai Huang, Li Tian, Yongxin Zhu, and Hui Wang. “An Efficient Small Traffic Sign Detection Method Based on YOLOv3”. Journal of Signal Processing Systems 93, no. 8 (2020): 899-911. https://doi.org/10.1007/s11265-020-01614-2.
1.
Wan J, Ding W, Zhu H, Xia M, Huang Z, Tian L, et al. An Efficient Small Traffic Sign Detection Method Based on YOLOv3. Journal of Signal Processing Systems. 2020;93(8):899-911.
Journal Categories
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Technology
Engineering (General)
Civil engineering (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Spatially Enhanced Bags of Visual Words Representation to Improve Traffic Signs Recognition | Journal of Signal Processing Systems |
| 3 | 2018 |
Traffic signal detection and classification in street views using an attention model | Computational Visual Media |
| 44 | 2018 |
| ImageNet Large Scale Visual Recognition Challenge | International Journal of Computer Vision |
| 12,571 | 2015 |
| The Pascal Visual Object Classes (VOC) Challenge | International Journal of Computer Vision |
| 6,216 | 2010 |
| 10.1109/TITS.2007.895311 | 2007 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| A review of small object detection based on deep learning | Neural Computing and Applications |
| 2024 | |
| A coarse-to-fine small object detection framework based on a background complexity classification strategy | Neural Computing and Applications |
| 2024 | |
| Deep Learning-Based Computer Vision Methods for Complex Traffic Environments Perception: A Review | Data Science for Transportation | 2024 | ||
Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review | Mathematics |
| 2024 | |
| YOLOF-F: you only look one-level feature fusion for traffic sign detection | The Visual Computer |
| 1 | 2023 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks 10
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs and was published in 2021. The most recent citation comes from a 2024 study titled A coarse-to-fine small object detection framework based on a background complexity classification strategy. This article reached its peak citation in 2023, with 9 citations. It has been cited in 13 different journals, 30% of which are open access. Among related journals, the Multimedia Tools and Applications cited this research the most, with 4 citations. The chart below illustrates the annual citation trends for this article.