[1]GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13(3):366-372.[doi:10.11992/tis.201706040]
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Detection of small and dense traffic signs in self-driving scenarios

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 3 Page number: 366-372 Column: 学术论文—机器感知与模式识别 Public date: 2018-05-05
Title:
Detection of small and dense traffic signs in self-driving scenarios
Author(s):
GE Yuanyuan1;  XU Youjiang1;  ZHAO Shuai2;  HAN Yahong1
1. School of Computer Science and Technology, Tianjin University, Tianjin 300350, China;
2. Data Resource Center, China Automotive Technology and Research Center, Tianjin 300300, China
Keywords:
traffic sign; object detection; deep learning; aggregate feature; CNN; feature map; region proposal; self-driving
CLC:
TP183
DOI:
10.11992/tis.201706040
Abstract:
In self-driving scenarios, the detection and recognition of traffic signs is critical to understanding the driving environment. The plethora of small traffic signs are hard to detect by the existing object detection technology. To detect these small traffic signs accurately, we propose the use of the shallow network VGG16 as the R-FCN’s backbone and the modification of the VGG16 network. There are mainly two improvements in the VGG16 network. First, we reduce the multiple zooming of feature maps, remove the feature maps behind the VGG16 network convolution conv4_3, and use the RPN network to extract the region proposal in the shallow convolution conv4_3 layer. We then concatenate the feature maps. The features of the layers of the convolutions conv4_1, conv4_2, and conv4_3 are adjoined to form an aggregated feature. The improved object detection framework can detect more small objects. We use a dataset of traffic signs to test the performance and mAP accuracy.
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