Traffic sign detection and recognition using fully convolutional network guided proposals

By Yingying Zhu, Chengquan Zhang, Duoyou Zhou, Xinggang Wang, Xiang Bai. 2016

219 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Brendan Dolan-Gavitt
2. Siddharth Garg
3. Junfan Wang
4. Mingyu Gao
5. Yi Chen
6. Tianyu Gu
7. Zhekang Dong

In Media Technology

1. Brendan Dolan-Gavitt
2. Siddharth Garg
3. Junfan Wang
4. Mingyu Gao
5. Yi Chen
6. Tianyu Gu
7. Zhekang Dong

In Human-Computer Interaction

1. Brendan Dolan-Gavitt
2. Siddharth Garg

Citations in the corpus

• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain (2017, methodology): “Transfer learning has also specifically been applied to the problem of traffic sign detection, the same scenario we discuss in Section 5, by Zhu et al. [44].”
• Improved YOLOv5 network for real-time multi-scale traffic sign detection (2021, methodology): “To demonstrate the advantages of the proposed method in traffic sign detection, we evaluated our method on TT100K and compared it with the original YOLOv5, YOLOv5-Lite [54], EfficientDet [55], YOLOv5-face [56], M2det [57], SSD, and YOLOv3 [58].”