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Proceedings Paper

Target recognition based on convolutional neural network
Author(s): Liqiang Wang; Xin Wang; Fubiao Xi; Jian Dong
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Paper Abstract

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Paper Details

Date Published: 15 November 2017
PDF: 7 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052A (15 November 2017); doi: 10.1117/12.2292889
Show Author Affiliations
Liqiang Wang, China Academy of Launch Vehicle Technology (China)
Xin Wang, China Academy of Launch Vehicle Technology (China)
Fubiao Xi, China Academy of Launch Vehicle Technology (China)
Jian Dong, Dalian Maritime Univ. (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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