[1] de la Escalera A, Moreno L E, Salichs M A et al. Road traffic sign detection and classification[J]. IEEE Transactions on Industrial Electronics, 44, 848-859(1997).

[2] Zaklouta F, Stanciulescu B. Real-time traffic-sign recognition using tree classifiers[J]. IEEE Transactions on Intelligent Transportation Systems, 13, 1507-1514(2012).

[3] Yao C, Wu F, Chen H J et al. Traffic sign recognition using HOG-SVM and grid search[C], 962-965(2014).

[4] Li L M, Li L, Tong R Q et al. Feature detection and matching for traffic sign images[C], 4628-4632(2012).

[5] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[6] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[7] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[8] Cao C Q, Wang B, Zhang W R et al. An improved faster R-CNN for small object detection[J]. IEEE Access, 7, 106838-106846(2019).

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

[10] Jin Y M, Fu Y S, Wang W Q et al. Multi-feature fusion and enhancement single shot detector for traffic sign recognition[J]. IEEE Access, 8, 38931-38940(2020).

[11] Zhou X Y, Wang D Q, Krahenbuhl P et al. Objects as points[EB/OL]. https://arxiv.org/abs/1904.07850

[12] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[13] Wei L J, Xu C, Li S Q et al. Traffic sign detection and recognition using novel center-point estimation and local features[J]. IEEE Access, 8, 83611-83621(2020).

[14] Ding T H, Feng K L, Li T P et al. An improved anchor-free detection method for traffic sign detection[C], 348-351(2021).

[15] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[16] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[17] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[18] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[19] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[20] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[21] Zhang Q L, Jiang Z Q, Lu Q S et al. Split to be slim: an overlooked redundancy in vanilla convolution[C], 3195-3201(2020).

[22] Zhu Z, Liang D, Zhang S H et al. Traffic-sign detection and classification in the wild[C], 2110-2118(2016).

[23] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[24] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934