The Vision-Based Data Reader in IoT System for Smart Factory

被引:14
|
作者
Hsu, Tse-Chuan [1 ]
Tsai, Yao-Hong [2 ,3 ]
Chang, Dong-Meau [4 ]
机构
[1] Soochow Univ, Dept Comp Sci & Informat Management, Taipei 111, Taiwan
[2] Hsuan Chuang Univ, Dept Visual Commun Design, Hsinchu 300, Taiwan
[3] Hsuan Chuang Univ, Dept Informat Management, Hsinchu 300, Taiwan
[4] Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Zhanjiang 524048, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 13期
关键词
digit recognition; Internet of Things; edge computing; deep learning; BIG DATA; INTERNET; THINGS;
D O I
10.3390/app12136586
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The proposed research is based on a real plastic injection factory for cutting board production. Most existing approaches for smart manufacturing tried to build the total solution of IoT by moving forward to the standard of industry 4.0. Under the cost considerations, this will not be acceptable to most factories, so we proposed the vision based technology to solve their immediate problem. Real-time machine condition monitoring is important for making great products and measuring line productivity or factory productivity. The study focused on a vision-based data reader (VDR) in edge computing for smart factories. A simple camera embedded in Field Programmable Gate Array (FPGA) was attached to monitor the screen on the control panel of the machines. Each end device was preprogrammed to capture images and process data on its own. The preprocessing step was then performed to have the normalized illumination of the captured image. A saliency map was generated to detect the required region for recognition. Finally, digit recognition was performed and the recognized digits were sent to the IoT system. The most significant contribution of the proposed VDR system used the compact deep learning model for training and testing purposes to fit the requirement of cost consideration and real-time monitoring in edge computing. To build the compact model, different convolution filters were tested to fit the performance requirement. Experimentations on a real plastic cutting board factory showed the improvement in manufacturing products by the proposed system and achieved a high digit recognition accuracy of 97.56%. In addition, the prototype system had low power and low latency advantages.
引用
收藏
页数:17
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