Image monitoring and recognition processing based on neural network

被引：0
作者：
Min L. [1 ]
Zhengkun Y. [1 ]
机构：
[1] Changsha Vocational and Technical College, Changsha, Hunan Province
来源：
| 1600年 / National Research Nuclear University卷 / 12期
关键词：
Back-propagation neural network; Image recognition; Information entropy;
D O I：
10.26583/sv.12.3.08
中图分类号：
学科分类号：
摘要：
With the development of economy and the abundance of material, people tend to travel. In the peak season of tourism, the scenic spots are crowded and easy to cause trample and safety problems. The traditional monitoring methods are rigid and have low recognition accu-racy. This paper briefly introduced the image monitoring and recognition system and the back-propagation (BP) neural network used for identifying the trampling risk areas in the monitoring images. After that, the image monitoring and recognition system was simulated by using MATLAB software, and it was compared with the traditional entropy method and state-of-the-art CNN. The results showed that the three methods could identify the area with trampling risk in the image, but the image monitoring and recognition system designed in this study was more comprehensive and had lower false alarm rate and shorter recognition time than the traditional information entropy method and state-of-the-art CNN. In summary, the image monitoring and recognition system designed in this study can efficiently and accu-rately identify the trampling risk areas in the monitoring images. © 2020 National Research Nuclear University. All rights reserved.
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页码：89 / 99
页数：10
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