<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid increase in computer users’ requirements for image information and image processing, and the rapid development of the intelligent process, the ability of the traditional visual system to process image information and data has been difficult to meet the needs of users. Therefore, in this article, we upgrade the vision system of smart cameras by introducing three network algorithm structures: convolutional neural network (CNN), LSTM and CNN-LSTM. We compare the classification performance of the three algorithms and evaluate them with three metrics: accuracy, precision and recall. The experimental results show that using the CNN algorithm, the accuracy of image information processing is 98.2%, the precision can reach 87.5% and the recall rate is 99.8%; the LSTM accuracy is 97.7%, its precision is 89.6% and its recall rate is 87.3%; its precision can be improved to 90.5% and the recall rate to 99.7%.</p>
</abstract>

With the rapid increase in computer users’ requirements for image information and image processing, and the rapid development of the intelligent process, the ability of the traditional visual system to process image information and data has been difficult to meet the needs of users. Therefore, in this article, we upgrade the vision system of smart cameras by introducing three network algorithm structures: convolutional neural network (CNN), LSTM and CNN-LSTM. We compare the classification performance of the three algorithms and evaluate them with three metrics: accuracy, precision and recall. The experimental results show that using the CNN algorithm, the accuracy of image information processing is 98.2%, the precision can reach 87.5% and the recall rate is 99.8%; the LSTM accuracy is 97.7%, its precision is 89.6% and its recall rate is 87.3%; its precision can be improved to 90.5% and the recall rate to 99.7%.

Design of an embedded machine vision system for smart cameras

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Design of an embedded machine vision system for smart cameras"}

With the rapid increase in computer users’ requirements for image information and image processing, and the rapid development of the intelligent process, the...

Design of an embedded machine vision system for smart cameras

Online veröffentlicht: 12. Sept. 2022

Seitenbereich: 145 - 156

Eingereicht: 16. Mai 2022

Akzeptiert: 15. Juni 2022

DOI: https://doi.org/10.2478/amns.2021.2.00245

Schlüsselwörter
image processing, vision system, optimisation design, CNN, LSTM

© 2023 Zhongxian Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Design of an embedded machine vision system for smart cameras

Online veröffentlicht: 12. Sept. 2022

Seitenbereich: 145 - 156

Eingereicht: 16. Mai 2022

Akzeptiert: 15. Juni 2022

DOI: https://doi.org/10.2478/amns.2021.2.00245

Schlüsselwörterimage processing, vision system, optimisation design, CNN, LSTM

© 2023 Zhongxian Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
image processing, vision system, optimisation design, CNN, LSTM