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基于物联网的人工智能图像检测系统设计与实现
引用本文:张华.基于物联网的人工智能图像检测系统设计与实现[J].应用声学,2017,25(2):15-15.
作者姓名:张华
作者单位:广州铁路职业技术学院 信息工程系
基金项目:2015年广东省公益研究与能力建设专项资金《基于标识分离与映射网络模型的物联网信息安全技术研究》
摘    要:物联网络的建立促使人工智能领域取得飞跃性进展。传统图像检测方法利用小波能算法进行背景与边缘噪声划分方式进行图像检测,存在低分辨率图像检测精度低、检测速度慢、缺乏图像深度分析等一系列问题。物联网人工智能发展迅速的环境下,提出基于物联网的人工智能图像检测系统设计。采用智能人工像素点特征采集技术(IAPCCT),对图像进行逐点特征提取,运用物联网丰富数据量资源与处理运算能力对采集图像像素点进行特征分析回馈,回馈信号经人工智能信号图像合成模块(AISIS),对信号做图像转换处理并输出分析结果完成图像检测。通过仿真实验测试证明,基于物联网的人工智能图像检测系统设计具有图像检测率高、识别准确度高、运行稳定、处理高效等优点。

关 键 词:物联网  人工智能  特征提取  图象检测
收稿时间:2016/9/14 0:00:00
修稿时间:2016/10/11 0:00:00

Based on the Internet of things of image detection system design and implementation of artificial intelligence
Abstract:the content of the network to establish leap progress led the field of artificial intelligence. Traditional algorithms of image detection method using wavelet can classify background noise and the edge image detection, in the form of low resolution images, such as low detecting precision and test speed is slow, lack of depth image analysis and a series of problems. Internet environment, the rapid development in artificial intelligence is proposed based on iot image detection system design of artificial intelligence. Using intelligent artificial pixel characteristic collection technology (IAPCCT), point to the image feature extraction, using Internet of rich data resources and deal with the power of collecting image pixel features analysis feedback, feedback signals by artificial intelligence image synthesis module (AISIS), the signal to do image conversion processing complete results of the analysis and the output image detection. Through the simulation test proves that the artificial intelligence image detection system design based on Internet of things has high image detection rate, high identification accuracy and stable operation, processing efficiency, etc.
Keywords:the Internet of things  Artificial intelligence  Feature extraction  Image detection
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