Opto-Electronic Engineering, Volume. 51, Issue 6, 240055-1(2024)

A traffic sign recognition method based on improved YOLOv5

Liguo Qu1...2,*, Xin Zhang1, Zibao Lu1, Yuling Liu1 and Guohao Chen3 |Show fewer author(s)
Author Affiliations
  • 1School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui 241002, China
  • 2Anhui Provincial Engineering Research Center for Information Fusion and Control of Intelligent Robots, Wuhu, Anhui 241002, China
  • 3Wuhan Mingke Rail Transit Equipment Co., Ltd., Wuhan, Hubei 430074, China
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    References(30)

    [1] R X Wang, J P Wu, H Xu. Overview of research and application on autonomous vehicle oriented perception system simulation. J Syst Simul, 34, 2507-2521(2022).

    [2] S Acharya, P K Nanda. Adjacent LBP and LTP based background modeling with mixed-mode learning for foreground detection. Pattern Anal Appl, 24, 1047-1074(2021).

    [3] F M Shao, X Q Wang, F J Meng et al. Real-time traffic sign detection and recognition method based on simplified Gabor wavelets and CNNs. Sensors, 18, 3192(2018).

    [4] Dominic Savio M Maria, T Deepa, A Bonasu et al. Image processing for face recognition using HAAR, HOG, and SVM algorithms. J Phys Conf Ser, 1964, 062023(2021).

    [5] C J C Burges. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discovery, 2, 121-167(1998).

    [6] P Thamilselvan. Lung cancer prediction and classification using adaboost data mining algorithm. Int J Comput Theory Eng, 14, 149-154(2022).

    [9] S Q Ren, K M He, R Girshick et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 39, 1137-1149(2017).

    [17] X Chen, D L Peng, Y Gu. Real-time object detection for UAV images based on improved YOLOv5s. Opto-Electron Eng, 49, 210372(2022).

    [18] J Yang, T Sun, W C Zhu et al. A lightweight traffic sign recognition model based on improved YOLOv5. IEEE Access, 11, 115998-116010(2023).

    [19] L Chen, J L Zhang, H Peng et al. Few-shot image classification via multi-scale attention and domain adaptation. Opto-Electron Eng, 50, 220232(2023).

    [20] J M Zhang, Z P Xie, J Sun et al. A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access, 8, 29742-29754(2020).

    [21] H B Zhang, L F Qin, J Li et al. Real-time detection method for small traffic signs based on Yolov3. IEEE Access, 8, 64145-64156(2020).

    [22] Y Guo, R L Liang, R M Wang. Cross-domain adaptive object detection based on CNN image enhancement in foggy conditions. Comput Eng Appl, 59, 187-195(2023).

    [24] Y D Wang, J C Guo, T B Wang. Algorithm for foggy-image pedestrian and vehicle detection. J Xidian Univ, 47, 70-77(2020).

    [25] B K Lang, B Lü, J Q Wu et al. A traffic sign detection model based on coordinate attention-bidirectional feature pyramid network. J Shenzhen Univ (Sci Eng), 40, 335-343(2023).

    [26] H Y Zhu, J N Han, Y Xu. Printed circuit board blemishes detection based on the improved YOLOv5s. Foreign Electron Meas Technol, 42, 152-159(2023).

    [27] Y W Wang, Y Lu, Y H Dou et al. Synchronous GPS spoofing Identification based on K-means clustering. J Electron Inf Technol, 45, 4137-4149(2023).

    [28] Z D Zhang, M L Tan, Z C Lan et al. CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5. Neural Comput Appl, 34, 10719-10730(2022).

    [30] T Y Lin, P Goyal, R Girshick et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell, 42, 318-327(2020).

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    Liguo Qu, Xin Zhang, Zibao Lu, Yuling Liu, Guohao Chen. A traffic sign recognition method based on improved YOLOv5[J]. Opto-Electronic Engineering, 2024, 51(6): 240055-1

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    Paper Information

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    Received: Mar. 7, 2024

    Accepted: Jun. 4, 2024

    Published Online: Oct. 21, 2024

    The Author Email: Qu Liguo (曲立国)

    DOI:10.12086/oee.2024.240055

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