| Title |
Industrial visual perception technology in Smart City |
| ID_Doc |
36246 |
| Authors |
Lv, ZH; Chen, DL |
| Title |
Industrial visual perception technology in Smart City |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.imavis.2020.104070 |
| Abstract |
In order to study the application effect and function of industrial visual perception technology in smart city, the image processing and quality evaluation system was constructed by using convolutional neural network (CNN) and Internet of things (IoT) technology. The system was simulated, and then the quality performance of image and video obtained by using industrial visual perception technology was processed and analyzed. The results show that in the analysis of image local optimization effect, it is found that the classification performance of all algorithms decreases with the increase of noise, and the performance of local anisotropic mode (LAP) is superior, which has strong robustness to rotation, illumination, and noise. In the analysis of image feature similarity effect, it is found that the chi square distance between Log Gabor features is positively correlated with the degree of image distortion, and the validity of the measurement method is verified. Further analysis of the video processing effect of industrial visual perception technology shows that the video processing effect of test algorithm is significantly better than that of HM16.8 by comparing the distortion performance of the two algorithms with different sequences, with low distortion and significantly improved performance. Therefore, through the research, it is found that the improved CNN algorithm is superior to other algorithms in image and video processing. (C) 2020 Elsevier B.V. All rights reserved. |
| Author Keywords |
Smart city; Industrial visual perception technology; Convolutional neural network; Quality evaluation; Image |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000611984800009 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics |
| Research Area |
Computer Science; Engineering; Optics |
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