@inproceedings{43b6505fccee431c91b9b1da97890626,
title = "Convolutional Neural Network Architecture for Semaphore Recognition",
abstract = "This paper proposes convolutional neural networks SRNet and Tiny-SRNet for human semaphore action recognition. SRNet is composed of 5 layers of convolution and 3 layers of fully connected layers. In addition to the first convolution layer., a batch normalization layer is added before each convolution layer. In order to enable deep learning algorithms to be applied to both mobile and embedded platforms., Tiny-SRNet removes the full connected layers in SRNet and replaces them with a convolutional layer and a global average pooling layer. The experimental results show that compared with the mainstream classification models AlexNet., GoogleNet and VGGI6., SRNet achieves the highest recognition rate of 98.9% on the semaphore dataset., and Tiny-SRNet compresses its model size to 1/24 of SRNet with a reduction of 1.7% accuracy.",
keywords = "BN, SRNet, Tiny-SRNet, semaphore",
author = "Wanchong Li and Yuliang Yang and Mengyuan Wang and Linhao Zhang and Mengyu Zhu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018 ; Conference date: 23-11-2018 Through 25-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICSESS.2018.8663885",
language = "English",
series = "Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS",
publisher = "IEEE Computer Society",
pages = "559--562",
editor = "Li Wenzheng and Babu, {M. Surendra Prasad}",
booktitle = "ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science",
address = "United States",
}