1 |
黄志勇, 孙光民, 李芳. 基于RGB视觉模型的交通标志分割. 微电子学与计算机, 2004, 21 (10): 147-148, 152.
|
|
HUANG Z Y , SUN G M , LI F . Traffic sign segment based on RGB vision model. Microelectronics & Computer, 2004, 21 (10): 147-148, 152.
|
2 |
BENALLAL M, MEUNIER J. Real-time color segmentation of road signs[C]//Proceedings of Canadian Conference on Electrical and Computer Engineering. Washington D. C., USA: IEEE Press, 2003: 1823-1826.
|
3 |
BARNES N , LOY G , SHAW D . The regular polygon detector. Pattern Recognition, 2010, 43 (3): 592- 602.
|
4 |
CHEN Y X , XIE Y , WANG Y L . Detection and recognition of traffic signs based on HSV vision model and shape features. Journal of Computers, 2013, 8 (5): 1366- 1370.
|
5 |
赵宏, 冯宇博. 基于CGS-Ghost YOLO的交通标志检测研究. 计算机工程, 2023, 49 (12): 194- 204.
doi: 10.19678/j.issn.1000-3428.0066520
|
|
ZHAO H , FENG Y B . Research on traffic sign detection based on CGS-Ghost YOLO. Computer Engineering, 2023, 49 (12): 194- 204.
doi: 10.19678/j.issn.1000-3428.0066520
|
6 |
ZHANG J M , YE Z , JIN X K , et al. Real-time traffic sign detection based on multiscale attention and spatial information aggregator. Journal of Real-Time Image Processing, 2022, 19 (6): 1155- 1167.
|
7 |
GAO E F , HUANG W G , SHI J J , et al. Long-tailed traffic sign detection using attentive fusion and hierarchical group softmax. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (12): 24105- 24115.
|
8 |
李嘉豪, 闵卫东, 陈炯缙, 等. 一种复杂场景下高精度交通标志检测模型. 计算机工程, 2023, 49 (11): 311- 320.
doi: 10.19678/j.issn.1000-3428.0066372
|
|
LI J H , MIN W D , CHEN J J , et al. A high precision traffic sign detection model in complex scenes. Computer Engineering, 2023, 49 (11): 311- 320.
doi: 10.19678/j.issn.1000-3428.0066372
|
9 |
WANG J F , CHEN Y , DONG Z K , et al. Improved YOLOv5 network for real-time multi-scale traffic sign detection. Neural Computing and Applications, 2023, 35 (10): 7853- 7865.
|
10 |
WANG J F , CHEN Y , JI X Y , et al. Vehicle-mounted adaptive traffic sign detector for small-sized signs in multiple working conditions. IEEE Transactions on Intelligent Transportation Systems, 2023, 25 (1): 710- 724.
|
11 |
HAN Y J , WANG F P , WANG W , et al. YOLO-SG: small traffic signs detection method in complex scene. The Journal of Supercomputing, 2024, 80 (2): 2025- 2046.
|
12 |
HU J, SHEN L, SUN G. Squeeze-and-Excitation networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D. C., USA: IEEE Press, 2018: 7132-7141.
|
13 |
ZHU Z, LIANG D, ZHANG S H, et al. Traffic-sign detection and classification in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washington D. C., USA: IEEE Press, 2016: 2110-2118.
|
14 |
ZHANG J , ZOU X , KUANG L D , et al. CCTSDB 2021: a more comprehensive traffic sign detection benchmark. Human-centric Computing and Information Sciences, 2022, 12, 289- 306.
|
15 |
REN S , HE K , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137- 1149.
doi: 10.1109/TPAMI.2016.2577031
|
16 |
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. Berlin, Germany: Springer, 2016: 21-37.
|
17 |
YANG C, HUANG Z H, WANG N Y. QueryDet: cascaded sparse query for accelerating high-resolution small object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D. C., USA: IEEE Press, 2022: 13668-13677.
|
18 |
SUN P Z, ZHANG R F, JIANG Y, et al. Sparse R-CNN: end-to-end object detection with learnable proposals[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D. C., USA: IEEE Press, 2021: 14454-14463.
|
19 |
|
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). Washington D. C., USA: IEEE Press, 2023: 7464-7475.
|
21 |
ZHANG R Y , ZHENG K M , SHI P C , et al. Traffic sign detection based on the improved YOLOv5. Applied Sciences, 2023, 13 (17): 9748.
|
22 |
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV). Washington D. C., USA: IEEE Press, 2017: 2980-2988.
|
23 |
|
24 |
HU J , WANG Z B , CHANG M J , et al. PSG-Yolov5: a paradigm for traffic sign detection and recognition algorithm based on deep learning. Symmetry, 2022, 14 (11): 2262.
|
25 |
QU S , YANG X , ZHOU H , et al. Improved YOLOv5-based for small traffic sign detection under complex weather. Scientific Reports, 2023, 13 (1): 16219.
|