Contextual and Multi-Scale Feature Fusion Network for Traffic Sign Detection
文献类型：会议论文
| 作者 | Zhang W(张威)1,2,3; Wang Q(王强)1,2; Fan HJ(范慧杰)1,2 ; Tang YD(唐延东)1,2
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| 出版日期 | 2020 |
| 会议日期 | October 10-13, 2020 |
| 会议地点 | Xi'an, China |
| 关键词 | Traffic sign detection contextual attention multi-scale feature convolutional neural network |
| 页码 | 13-17 |
| 英文摘要 | The traffic sign detection, as an important part of the automatic driving system, requires high accuracy. In this paper, we proposed an end-to-end deep learning network, named the Contextual and Multi-Scale Feature Fusion Network, for traffic sign detection. The model consists of two sub-networks: the Weighted Multi-scale Feature Learning network (W-net) and the Contextual-Attention Learning network (C-net). The W-net extracts multi-scale features and calculates the weights of each feature map layer to detect traffic signs under different scales. The C-net learns the contextual attention mask of interference items and concatenates it with the multi-scale feature, which reduce the detection false efficiently. Compared with several state-of-the-art traffic sign detection methods, our proposed model outperforms others on extensive quantitative and qualitative experiments. |
| 产权排序 | 1 |
| 会议录 | Proceedings of 10th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2020
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-9009-9 |
| WOS记录号 | WOS:000646188000003 |
| 源URL | [http://ir.sia.cn/handle/173321/28166]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Zhang W(张威) |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Insititutes of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Institutes for Robotics and Intelligent Manufacturing, CAS, Shenyang 110016, China 3.University of Chinese Academy of Sciences, 100049, China |
| 推荐引用方式 GB/T 7714 | Zhang W,Wang Q,Fan HJ,et al. Contextual and Multi-Scale Feature Fusion Network for Traffic Sign Detection[C]. 见:. Xi'an, China. October 10-13, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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