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Zhu, Z., Liang, D., Zhang, S., et al. (2016) Traffic-Sign Detection and Classification in the Wild. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 27-30 June 2016, 2110-2118. https://doi.org/10.1109/CVPR.2016.232

has been cited by the following article:

• TITLE: Research on Traffic Sign Detection Based on Improved YOLOv8

  AUTHORS: Zhongjie Huang, Lintao Li, Gerd Christian Krizek, Linhao Sun

  KEYWORDS: Traffic Sign Detection, Small Object Detection, YOLOv8, Feature Fusion

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.7, July 31, 2023

  ABSTRACT: Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher.