|本期目录/Table of Contents|

[1]范 旭,葛 笑,夏凯旋,等.基于Retinex-Net的集气站指针仪表暗光图像增强方案[J].工业仪表与自动化装置,2022,(05):109-115.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.020]
　FAN Xu,GE Xiao,XIA Kaixuan,et al.Low-light image enhancement and reading of pointer meter in gas gathering station based on Retinex-Net[J].Industrial Instrumentation & Automation,2022,(05):109-115.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.020]
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基于Retinex-Net的集气站指针仪表暗光图像增强方案

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年05期
页码:
109-115
栏目:
出版日期:
2022-10-15

文章信息/Info

Title:
Low-light image enhancement and reading of pointer meter in gas gathering station based on Retinex-Net
文章编号:
1000-0682（2022）05-0000-00
作者:
范 旭1; 葛 笑2; 夏凯旋1; 祝忠钲2; 赵有龙1; 林建伟1
1.中海油能源发展股份有限公司采油服务分公司，天津 300452；
2.东南大学 自动化学院，江苏 南京 210018
Author(s):
FAN Xu1;  GE Xiao2;  XIA Kaixuan1;  ZHU Zhongzheng2;  ZHAO Youlong1;  LIN Jianwei1
1. Oil Production Service Branch of CNOOC Energy Development Co., Ltd. Tianjin 300452, China；
2. School of automation, Southeast University, Jiangsu Nanjing 210018, China
关键词:
暗光图像增强; Retinex; 神经网络; 读数识别
Keywords:
low-light image enhancement;  Retinex;  neural network;  recognition of readings
分类号:
TP391.4
DOI:
10.19950/j.cnki.cn61-1121/th.2022.05.020
文献标志码:
A
摘要:
集气站在引入巡检机器人进行仪表检测与识别任务时，对图像质量及处理实时性要求较高，但实际工作过程中因光照、遮挡等因素，经常拍摄到难以识别的暗光图像，传统暗光增强方法难以同时满足增强效果与实时性需求。基于Retinex理论及神经网络，文中引入Retinex-Net，在集气站实地拍摄的正常光/暗光图像集上进行测试，实现对暗光仪表图像的增强，在恢复仪表的真实色彩的同时尽可能的保留指针等细节信息。将该算法与CLAHE、MSRCR、AutoMSRCR算法在处理效果与处理速度上进行了对比，分析了Retinex-Net用于暗光仪表图像增强的优越性；对比处理前后仪表图像读数情况，证明了算法的实用性与鲁棒性。
Abstract:
Instrument detection and identification in gas gathering station require real-time processing and high-quality images when inspection robot is introduced. Because factors like illumination and occlusion, the robot captures low-light images frequently in actual working process. Traditional methods could not meet the requirements of enhancement effect and real-time performance at the same time. Based on the Retinex theory and neural network, we applied Retinex-Net to normal/low-light image set taken on the station, and realized the enhancement of the low-light images, which restored the true color of the instrument as same as remained details such as the pointer. Comparing our algorithm with CLAHE, MSRCR and AutoMSRCR in processing effect and speed, we analyzed the superiority of Retinex-Net. In addition, the practicability and robustness of the algorithm are proved by comparing the readings of meters before and after processing.

参考文献/References:

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相似文献/References:

备注/Memo

备注/Memo:
收稿日期：2022-05-02

作者简介：
范旭（1985），男，大学本科，从事非常规油气及海上FPSO生产运营工作。

通信作者：
葛笑（2000），男，硕士研究生，从事人工智能与模式识别相关研究。
更新日期/Last Update: 1900-01-01