[1] [1] FITREK T, LONARI S. Traffic sign detection and recognition using neural networks and histogram based selection of segmentation method[C]//Proceedings of ELMAR-2011, September 14-16, 2011, Zadar, Croatia. New York: IEEE, 2011: 51-54.

[2] [2] SHOPA P, SUMITHA N, PATRA P S K. Traffic sign detection and recognition using OpenCV[C]//Proceedings of International Conference on Information Communication and Embedded Systems (ICICES 2014), February 27-28, 2014, Chennai, India. New York: IEEE, 2014: 1-6.

[3] [3] MIU X D, LI S M, SHEN X, et al. Traffic sign detection method based on visual adversarial color[J]. Journal of instrumentation, 2012, 33(01): 56-61.

[4] [4] ZHANG J, LV Y, TAO J, et al. A robust real-time anchor-free traffic sign detector with one-level feature[J]. IEEE transactions on emerging topics in computational intelligence, 2024.

[5] [5] BHATT N, LALDAS P, LOBO V B. A real-time traffic sign detection and recognition system on hybrid dataset using CNN[C]//Proceedings of 2022 7th International Conference on Communication and Electronics Systems (ICCES), July 28-30, 2022, Coimbatore, India. New York: IEEE, 2022: 1354-1358.

[6] [6] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 580-587.

[7] [7] CUI H, WANG Y, LI Y, et al. An improved combination of faster R-CNN and U-Net network for accurate multi-modality whole heart segmentation[J]. IEEE journal of biomedical and health informatics, 2023.

[8] [8] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 11-18, 2015, Santiago, Chile. New York: IEEE, 2015: 1440-1448.

[9] [9] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of Computer Vision-ECCV 2016: 14th European Conference, October 11-14, 2016, Amsterdam, The Netherlands. Heidelberg: Springer International Publishing, 2016: 21-37.

[10] [10] ZHANG G, WANG Y M, PENG C L. Traffic sign detection algorithm based on lightweight SSD[J]. Experimental technology and management, 2024, 41(01): 63-69.

[11] [11] ZHU N K, ZHANG S D, WANG H W, et al. Traffic sign detection based on multi-scale YOLOv5[J]. Radio engineering, 2024, 54(03): 623-632.

[12] [12] LI Y W, FU R, LIU F, et al. Lightweight traffic sign detection algorithm based on improved YOLOv7[J]. Journal of Taiyuan University of Technology, 2024, 55(01): 195-203. (in Chinese)

[13] [13] ZHANG L J, FANG J J, LIU Y X, et al. CR-YOLOv8: multiscale object detection in traffic sign images[J]. IEEE access, 2023, 12: 219-228.

[14] [14] TIAN P, MAO L. Improved road traffic sign object detection algorithm based on YOLOv8[J]. Computer engineering and applications, 2024: 1-14.

[15] [15] DAI J, QI H, XIONG Y, et al. Deformable convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 764-773.

[16] [16] TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 14-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 10781-10790.

[17] [17] YANG L, ZHANG R Y, LI L, et al. SimAM: a simple, parameter-free attention module for convolutional neural networks[C]//Proceedings of the International Conference on Machine Learning (ICML), July 18-24, 2021, Virtual. PMLR, 2021: 11863-11874.

[18] [18] ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.

[19] [19] LIU H B, ZHANG Y B, ZHOU K, et al. Traffic sign detection algorithm based on improved YOLOv5-S[J]. Journal of computer engineering & applications, 2024, 60(5): 200-209.

[20] [20] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 18-22, 2023, Vancouver, BC, Canada. New York: IEEE, 2023: 7464-7475.