文献类型：会议论文

英文题名：A traffic sign recognition model with only 140 kb

作者：Dawei, Luo[1]; Jianjun, Fang[2]; Dengfeng, Yao[1]

第一作者：Dawei, Luo

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

会议论文集：RSVT 2019 - 2019 International Conference on Robotics Systems and Vehicle Technology

会议日期：October 18, 2019 - October 20, 2019

会议地点：Wuhan, China

语种：英文

外文关键词：Feature extraction - Image classification - Pattern recognition - Robotics - Traffic signs

摘要：To design a sign recognition model with low computational complexity and Low parameter quantity, we uses Group Convolution to compress the parameters, and designs extreme block to solve the problem that the number of input channels of Group Convolution must be equal to the number of output channels and that the feature can not be extracted across channels. In this paper, the number of convolution kernels is set according to the number of classifications. Finally, the original 30 MB CifarNet is compressed into a 140 KB classification model. And we tested it on the BelgiumTS Dataset. The experimental test results show that after the model size is compressed to the original 1/220, top1 is not reduced, but it is increased by 87.31%, and top5 is increased by 0.5%. Experiments prove that the compression strategy is effective. And the experiment also explored the relationship between the number of convolution kernels and the number of classifications. ? 2019 Association for Computing Machinery.