Frontiers in Signal Processing
FSP
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Volume 3, Number 4, October 2019
Multi-Scale CapsNet: A Novel Traffic Sign Recognition Method
Download PDF (660.3
KB)PP.
93-99
, Pub. Date:October 10, 2019
DOI:
10.22606/fsp.2019.34005
Author(s)
Gongbin Chen, Yansong Deng
Affiliation(s)
Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission, Chengdu, China
Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission, Chengdu, China
Abstract
Convolutional Neural Networks (CNNs) have performed very well on image classification
tasks, but CNNs is insensitive to detailed image information and requires a large amount of training
data and time. Capsule Networks(CapsNets) can solve this problem very well, but the Baseline
CapsNet model is very shallow, and the extraction of low-level features is not enough. We propose a
Multi-Scale Capsule Network (Multi-Scale CapsNet), by extracting the low-level features of images
with multi-channel convolution of multiple convolution kernels, so extracted features are more
diverse, then passing from the bottom layer to the upper layer in the form of a "capsule", which
encapsulats the multidimensional features of the image in the form of a vector, thus the features
are saved in the network, rather than being recovered after being lost. In the German Traffic Sign
Recognition Benchmark(GTSRB), we obtained competitive results with the accuracy of 99.4%,
which is better than the human performance of 98.81% and the Multi-Scale Convolutional Neural
Network(MS-CNN) of 97.33%.
Keywords
CapsNets, multi-scale CapsNet, traffic sign recognition, multi-scale convolution, CNN
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