Open AccessFast object detection based on binary deep convolution neural networks
Author(s) -
Sun Siyang,
Yin Yingjie,
Wang Xingang,
Xu De,
Wu Wenqi,
Gu Qingyi
Publication year - 2018
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2018.1026
Subject(s) - convolution (computer science) , convolutional neural network , pascal (unit) , computer science , binary number , object detection , artificial intelligence , pattern recognition (psychology) , algorithm , feature (linguistics) , object (grammar) , deep learning , artificial neural network , mathematics , arithmetic , linguistics , philosophy , programming language
In this study, a fast object detection algorithm based on binary deep convolution neural networks (CNNs) is proposed. Convolution kernels of different sizes are used to predict classes and bounding boxes of multi‐scale objects directly in the last feature map of a deep CNN. In this way, rapid object detection with acceptable precision loss is achieved. In addition, binary quantisation for weight values and input data of each layer is used to squeeze the networks for faster object detection. Compared to full‐precision convolution, the proposed binary deep CNNs for object detection results in 62 times faster convolutional operations and 32 times memory saving in theory, what's more, the proposed method is easy to be implemented in embedded computing systems because of the binary operation for convolution and low memory requirement. Experimental results on Pascal VOC2007 validate the effectiveness of the authors’ proposed method.