• Opto-Electronic Engineering
  • Vol. 51, Issue 6, 240055-1 (2024)
Liguo Qu1,2,*, Xin Zhang1, Zibao Lu1, Yuling Liu1, and Guohao Chen3
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
  • show less
    DOI: 10.12086/oee.2024.240055 Cite this Article
    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 Copy Citation Text show less
    References

    [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).

    [7] R Girshick, J Donahue, T Darrell et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 580-587(2014). https://doi.org/10.1109/CVPR.2014.81

    [8] R Girshick. Fast R-CNN, 1440-1448(2015). https://doi.org/10.1109/ICCV.2015.169

    [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).

    [10] W Liu, D Anguelov, D Erhan et al. SSD: single shot MultiBox detector, 21-37(2016). https://doi.org/10.1007/978-3-319-46448-0_2

    [11] A Bochkovskiy, C Y Wang, H Y M Liao. YOLOv4: optimal speed and accuracy of object detection(2020). https://arxiv.org/abs/2004.10934v1.

    [13] Z Ge, S T Liu, F Wang et al. YOLOX: exceeding YOLO series in 2021(2021). https://arxiv.org/abs/2107.08430.

    [14] C Y Li, L L Li, H L Jiang et al. YOLOv6: a single-stage object detection framework for industrial applications(2022). https://arxiv.org/abs/2209.02976.

    [15] C Y Wang, A Bochkovskiy, H Y M Liao. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 7464-7475(2023). https://doi.org/10.1109/CVPR52729.2023.00721

    [16] D Reis, J Kupec, J Hong et al. Real-time flying object detection with YOLOv8(2024). https://arxiv.org/abs/2305.09972.

    [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).

    [23] H B Lin, J L Zhou, M Z Chen. Traffic sign detection algorithm based on improved YOLOv4, 2156-2160(2022). https://doi.org/10.1109/ITAIC54216.2022.9836923

    [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).

    [29] C Y Chen, M Y Liu, O Tuzel et al. R-CNN for small object detection, 214-230(2016). https://doi.org/10.1007/978-3-319-54193-8_14

    [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).

    [31] S Woo, J Park, J T Lee et al. CBAM: convolutional block attention module, 3-19(2018). https://doi.org/10.1007/978-3-030-01234-2_1

    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
    Download Citation