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Proceedings Paper

Research and application of object recognition method based on depth neural network
Author(s): Qiong Li; Xiaofeng Ma
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Paper Abstract

As computer performance continues to improve, new deep learning algorithms emerge in an endless stream. Object recognition (target detection) is one of the influential research directions in the field of computer vision. The traditional object recognition method has the following problems: 1. The generation of the target suggestion frame has a cumbersome effect on the detection speed, accuracy and redundancy; 2. Artificially extracting the image features cannot guarantee the quality of the feature; 3. Using the traditional machine learning method for feature classification is low; 4.slow detection speed and low accuracy. In this paper, the DenseNet structure is used to improve the recognition accuracy, and the SSD is used to improve the detection speed. At the same time, with the classification detection technology and the jump connection technology used in the network, the experiment shows that the target detection efficiency is further improved.

Paper Details

Date Published: 14 February 2020
PDF: 5 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301H (14 February 2020); doi: 10.1117/12.2539410
Show Author Affiliations
Qiong Li, Wuhan Institute of Technology (China)
Xiaofeng Ma, Wuhan Institute of Technology (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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