TY - GEN
T1 - A convolutional neural network approach for semaphore flag signaling recognition
AU - Zhao, Qian
AU - Li, Yawei
AU - Yang, Ning
AU - Yang, Yuliang
AU - Zhu, Mengyu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/27
Y1 - 2017/3/27
N2 - This paper proposes a recognition approach for Semaphore flag signaling (SFS). We use the improved convolutional neural network (CNN) to classify the SFS. In the experiment we made Semaphore flag signaling system (SFSS), which based on CNN. The image can be directly input into the SFSS. Each alphabetic character or control signal is indicated by a particular flag pattern. We shoot the SFS videos by a monocular camera. The dataset is divided into five SFS classes. The improved CNN uses the Relu activation function, the max-pooling methods. It's alway use SFS data whitening and grayscale preprocessing methods. The improved CNN provides for partial invariance to different light, angles, scenes, and a group of people. The result shows that our approach classifies five SFS classes with 99.95% accuracy.
AB - This paper proposes a recognition approach for Semaphore flag signaling (SFS). We use the improved convolutional neural network (CNN) to classify the SFS. In the experiment we made Semaphore flag signaling system (SFSS), which based on CNN. The image can be directly input into the SFSS. Each alphabetic character or control signal is indicated by a particular flag pattern. We shoot the SFS videos by a monocular camera. The dataset is divided into five SFS classes. The improved CNN uses the Relu activation function, the max-pooling methods. It's alway use SFS data whitening and grayscale preprocessing methods. The improved CNN provides for partial invariance to different light, angles, scenes, and a group of people. The result shows that our approach classifies five SFS classes with 99.95% accuracy.
KW - Convolutional neural network
KW - activation function
KW - data preprocessing
KW - semaphore flag signaling system
UR - http://www.scopus.com/inward/record.url?scp=85018700093&partnerID=8YFLogxK
U2 - 10.1109/SIPROCESS.2016.7888306
DO - 10.1109/SIPROCESS.2016.7888306
M3 - Conference contribution
AN - SCOPUS:85018700093
T3 - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
SP - 466
EP - 470
BT - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
Y2 - 13 August 2016 through 15 August 2016
ER -