LLTH-YOLOv5: A Real-Time Traffic Sign Detection Algorithm for Low-Light Scenes-Reference-Cited by-同舟云学术

LLTH-YOLOv5: A Real-Time Traffic Sign Detection Algorithm for Low-Light Scenes

Published:2024-01-05 Issue:1 Volume:7 Page:121-137
ISSN:2096-4250
Container-title:Automotive Innovation
language:en
Short-container-title:Automot. Innov.

Author:

Sun Xiaoqiang^ORCID,Liu Kuankuan^ORCID,Chen Long^ORCID,Cai Yingfeng^ORCID,Wang Hai^ORCID

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42154-023-00249-w.pdf

Reference43 articles.

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2. Wang, W., Luo, S., Geng, G.Q., Liu, J.: Pedestrian trajectory and intention estimation under low brightness based on human key points. J. Jiangsu Univ. 43(4), 400–406 (2022)

3. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

4. Bochkovskiy, A., Wang, C.Y., Liao, H.Y. M.: YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934 (2020)

5. Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517–6525 (2017)

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