Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN

Fule Ji; Yanlong Xi

doi:doi:10.11648/j.eas.20220706.13

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Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN

Fule Ji, Yanlong Xi

Published in Engineering and Applied Sciences (Volume 7, Issue 6)

Received: 24 November 2022 Accepted: 8 December 2022 Published: 15 December 2022

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Abstract

Intelligent manufacturing relies heavily on industrial vision, and visual algorithms are rapidly being applied in the industry. However, industrial controllers are primarily used for logic control with deterministic execution cycles, and the uncertainty of vision code execution time strongly correlated with input affects their stability. To adjust the scanning cycle of the system in time to ensure system stability, an algorithm that can predict the time required for the vision code to process the target image is needed. In this paper, we analyze the weakness of traditional convolutional neural network models (CNN) and propose a multi-level and multi-scale CNN model (MLMS-CNN) for vision code execution time prediction. Instead of typical convolutional layers, we design an architecture to collect multi-scale features from the input feature maps. Moreover, a hierarchical structure is designed to reduce the loss of intermediate feature utilization by fusing features from different abstraction levels. We extract image features from images and runtime features from vision code blocks, then compare MLMS-CNN to six standard regression models, all of which are trained with the extracted features as input and the actual execution results of the visual code as output. The experimental results show that our model achieves better performance and stability.

Published in	Engineering and Applied Sciences (Volume 7, Issue 6)
DOI	10.11648/j.eas.20220706.13
Page(s)	93-99
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Deep Learning, Performance Prediction, Vision Code

References

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Fule Ji, Yanlong Xi. (2022). Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN. Engineering and Applied Sciences, 7(6), 93-99. https://doi.org/10.11648/j.eas.20220706.13

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ACS Style

Fule Ji; Yanlong Xi. Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN. Eng. Appl. Sci. 2022, 7(6), 93-99. doi: 10.11648/j.eas.20220706.13

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Fule Ji, Yanlong Xi. Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN. Eng Appl Sci. 2022;7(6):93-99. doi: 10.11648/j.eas.20220706.13

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@article{10.11648/j.eas.20220706.13,
  author = {Fule Ji and Yanlong Xi},
  title = {Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN},
  journal = {Engineering and Applied Sciences},
  volume = {7},
  number = {6},
  pages = {93-99},
  doi = {10.11648/j.eas.20220706.13},
  url = {https://doi.org/10.11648/j.eas.20220706.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20220706.13},
  abstract = {Intelligent manufacturing relies heavily on industrial vision, and visual algorithms are rapidly being applied in the industry. However, industrial controllers are primarily used for logic control with deterministic execution cycles, and the uncertainty of vision code execution time strongly correlated with input affects their stability. To adjust the scanning cycle of the system in time to ensure system stability, an algorithm that can predict the time required for the vision code to process the target image is needed. In this paper, we analyze the weakness of traditional convolutional neural network models (CNN) and propose a multi-level and multi-scale CNN model (MLMS-CNN) for vision code execution time prediction. Instead of typical convolutional layers, we design an architecture to collect multi-scale features from the input feature maps. Moreover, a hierarchical structure is designed to reduce the loss of intermediate feature utilization by fusing features from different abstraction levels. We extract image features from images and runtime features from vision code blocks, then compare MLMS-CNN to six standard regression models, all of which are trained with the extracted features as input and the actual execution results of the visual code as output. The experimental results show that our model achieves better performance and stability.},
 year = {2022}
}

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TY  - JOUR
T1  - Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN
AU  - Fule Ji
AU  - Yanlong Xi
Y1  - 2022/12/15
PY  - 2022
N1  - https://doi.org/10.11648/j.eas.20220706.13
DO  - 10.11648/j.eas.20220706.13
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 93
EP  - 99
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20220706.13
AB  - Intelligent manufacturing relies heavily on industrial vision, and visual algorithms are rapidly being applied in the industry. However, industrial controllers are primarily used for logic control with deterministic execution cycles, and the uncertainty of vision code execution time strongly correlated with input affects their stability. To adjust the scanning cycle of the system in time to ensure system stability, an algorithm that can predict the time required for the vision code to process the target image is needed. In this paper, we analyze the weakness of traditional convolutional neural network models (CNN) and propose a multi-level and multi-scale CNN model (MLMS-CNN) for vision code execution time prediction. Instead of typical convolutional layers, we design an architecture to collect multi-scale features from the input feature maps. Moreover, a hierarchical structure is designed to reduce the loss of intermediate feature utilization by fusing features from different abstraction levels. We extract image features from images and runtime features from vision code blocks, then compare MLMS-CNN to six standard regression models, all of which are trained with the extracted features as input and the actual execution results of the visual code as output. The experimental results show that our model achieves better performance and stability.
VL  - 7
IS  - 6
ER  -

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Author Information

Fule Ji

College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Yanlong Xi

College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

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Fule Ji, Yanlong Xi. (2022). Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN. Engineering and Applied Sciences, 7(6), 93-99. https://doi.org/10.11648/j.eas.20220706.13

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ACS Style

Fule Ji; Yanlong Xi. Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN. Eng. Appl. Sci. 2022, 7(6), 93-99. doi: 10.11648/j.eas.20220706.13

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AMA Style

Fule Ji, Yanlong Xi. Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN. Eng Appl Sci. 2022;7(6):93-99. doi: 10.11648/j.eas.20220706.13

Copy | Download

@article{10.11648/j.eas.20220706.13,
  author = {Fule Ji and Yanlong Xi},
  title = {Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN},
  journal = {Engineering and Applied Sciences},
  volume = {7},
  number = {6},
  pages = {93-99},
  doi = {10.11648/j.eas.20220706.13},
  url = {https://doi.org/10.11648/j.eas.20220706.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20220706.13},
  abstract = {Intelligent manufacturing relies heavily on industrial vision, and visual algorithms are rapidly being applied in the industry. However, industrial controllers are primarily used for logic control with deterministic execution cycles, and the uncertainty of vision code execution time strongly correlated with input affects their stability. To adjust the scanning cycle of the system in time to ensure system stability, an algorithm that can predict the time required for the vision code to process the target image is needed. In this paper, we analyze the weakness of traditional convolutional neural network models (CNN) and propose a multi-level and multi-scale CNN model (MLMS-CNN) for vision code execution time prediction. Instead of typical convolutional layers, we design an architecture to collect multi-scale features from the input feature maps. Moreover, a hierarchical structure is designed to reduce the loss of intermediate feature utilization by fusing features from different abstraction levels. We extract image features from images and runtime features from vision code blocks, then compare MLMS-CNN to six standard regression models, all of which are trained with the extracted features as input and the actual execution results of the visual code as output. The experimental results show that our model achieves better performance and stability.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Vision Code Execution Time Prediction Based on Multi-level and Multi-scale CNN
AU  - Fule Ji
AU  - Yanlong Xi
Y1  - 2022/12/15
PY  - 2022
N1  - https://doi.org/10.11648/j.eas.20220706.13
DO  - 10.11648/j.eas.20220706.13
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 93
EP  - 99
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20220706.13
AB  - Intelligent manufacturing relies heavily on industrial vision, and visual algorithms are rapidly being applied in the industry. However, industrial controllers are primarily used for logic control with deterministic execution cycles, and the uncertainty of vision code execution time strongly correlated with input affects their stability. To adjust the scanning cycle of the system in time to ensure system stability, an algorithm that can predict the time required for the vision code to process the target image is needed. In this paper, we analyze the weakness of traditional convolutional neural network models (CNN) and propose a multi-level and multi-scale CNN model (MLMS-CNN) for vision code execution time prediction. Instead of typical convolutional layers, we design an architecture to collect multi-scale features from the input feature maps. Moreover, a hierarchical structure is designed to reduce the loss of intermediate feature utilization by fusing features from different abstraction levels. We extract image features from images and runtime features from vision code blocks, then compare MLMS-CNN to six standard regression models, all of which are trained with the extracted features as input and the actual execution results of the visual code as output. The experimental results show that our model achieves better performance and stability.
VL  - 7
IS  - 6
ER  -

Copy | Download