Application of image detection techniques in telecom operation and maintenance system

Bo FAN; Yun QIU; Lei SHEN

doi:10.11959/j.issn.1000-0801.2019086

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Application of image detection techniques in telecom operation and maintenance system

Running the ai column | 更新时间：2024-06-05

- Application of image detection techniques in telecom operation and maintenance system
- Telecommunications Science Vol. 35, Issue 4, Pages: 146-152(2019)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2019086
  CLC： TN929.5
- Published Online：2019-04，
  
  Published：20 April 2019
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Bo FAN, Yun QIU, Lei SHEN. Application of image detection techniques in telecom operation and maintenance system[J]. Telecommunications science, 2019, 35(4): 146-152.
DOI：

Bo FAN, Yun QIU, Lei SHEN. Application of image detection techniques in telecom operation and maintenance system[J]. Telecommunications science, 2019, 35(4): 146-152. DOI： 10.11959/j.issn.1000-0801.2019086.

摘要

随着电信行业的发展，持续增长的宽带业务需要更高的人工运维成本。在电信运维的装维质检中

需要人工识别施工现场图片以评估装维质量。传统的装维人工质检存在检测准确率低且人力成本高的问题。近年来基于深度学习的图像识别技术不断发展，应用领域不断拓宽。若采用图像识别技术，则可显著节约人力成本并提高准确率。将多种图像识别技术应用在电信运维业务中的装维场景，并分析比较各种图像识别技术的性能与准确率。基于分析，进一步提出了一种融合的图像识别模型，应用于电信运维的质检流程，显著提高了运维图片识别准确率和质检效率。

Abstract

With the development of telecom industry

growing web service operations need more people to maintain the quality of the service.In the branch of telecom operation service

installation and maintenance service

the service quality is evaluated by human through assessing picture of construction site.The traditional way of evaluating quality of operation service brings low accuracy and high labor cost.In recent years

image recognition technology developed well with the deep learning raised

the related application field were broaden.If image recognition techniques were applied in telecom operation and maintenance system

the labor cost would be saved and the recognition accuracy would be improved.Several image recognition techniques were applied in installation quality assessment of telecom operation and maintenance system

and the performance and accuracy were analyzed.Based on analysis

a combining image recognition model was further proposed.The image recognition accuracy and efficiency of quality assessment was improved.

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Keywords

references

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HE K , ZHANG X Y , RENS Q , et al . Spatial pyramid poling in deep convolutional networks for visual recognition [J ] . IEEE Trans on Pattern Analysis and Machine Intelligence , 2015 , 37 ( 9 ): 1904 - 1914 .

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