[1]王凯旋,任福继,倪红军,等.面向电力设备红外图像的温度值识别算法[J].智能系统学报,2022,17(3):617-624.[doi:10.11992/tis.202105043]
WANG Kaixuan,REN Fuji,NI Hongjun,et al.Temperature value recognition algorithm for the infrared image of power equipment[J].CAAI Transactions on Intelligent Systems,2022,17(3):617-624.[doi:10.11992/tis.202105043]
点击复制
WANG Kaixuan,REN Fuji,NI Hongjun,et al.Temperature value recognition algorithm for the infrared image of power equipment[J].CAAI Transactions on Intelligent Systems,2022,17(3):617-624.[doi:10.11992/tis.202105043]
面向电力设备红外图像的温度值识别算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
617-624
栏目:
人工智能院长论坛
出版日期:
2022-05-05
- Title:
- Temperature value recognition algorithm for the infrared image of power equipment
- Keywords:
- power equipment; infrared image; adaptive threshold; image segmentation; character recognition; convolutional neural network; defect detection; simulation system
- 分类号:
- TP391
- 摘要:
- 为了快速准确地识别出红外图像中温度值实现缺陷检测，提出了面向电力设备红外图像的温度值识别算法。针对温度值区域背景复杂的问题，根据红外图像直方图自适应确定阈值进行预处理；结合轮廓与相对位置信息，准确定位温度值区域，并实现字符分割；建立温度值图像数据集，采用卷积神经网络进行训练和测试；基于MATLAB的App Designer模块，设计温度值识别与记录系统。结果证明，该算法对温度值识别准确率达到98.6%，高于传统的字符识别算法，能够实现快速识别与准确记录温度值，有效降低了电力巡检人员的劳动强度。
- Abstract:
- To quickly and accurately recognize the temperature value of an infrared image and realize defect detection, we propose a temperature value recognition algorithm for the infrared image of power equipment. Owing to the complex background of the temperature value region, the adaptive threshold is determined for preprocessing based on the infrared image histogram. Combined with the contour and relative position information, the precise location of the temperature value is segmented. The infrared image temperature values dataset is established, trained, and tested by the convolutional neural network. The temperature value recognition and recording system are designed based on the App Designer module of MATLAB. The experiment demonstrates that the accuracy of the proposed method reaches 98.6%, which is higher than the traditional character recognition algorithm. The proposed method can quickly and accurately recognize and record the temperature value, effectively reducing the labor intensity of power inspectors.
- 参考文献/References:
-
[1] CHEN Junwen, LIU Zhigang, WANG Hongrui, et al. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network[J]. IEEE transactions on instrumentation and measurement, 2018, 67(2): 257–269.
[2] 彭向阳, 钟清, 饶章权, 等. 基于无人机紫外检测的输电线路电晕放电缺陷智能诊断技术[J]. 高电压技术, 2014, 40(8): 2292–2298
PENG Xiangyang, ZHONG Qing, RAO Zhangquan, et al. Intelligent diagnostic techniques of transmission lines corona discharge defect based on ultraviolet detection from unmanned aerial vehicle[J]. High voltage engineering, 2014, 40(8): 2292–2298
[3] TAO Xian, ZHANG Dapeng, WANG Zihao, et al. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks[J]. IEEE transactions on systems, man, and cybernetics:systems, 2020, 50(4): 1486–1498.
[4] 王季峥, 尹丽菊, 咸日常, 等. 基于改进SLIC算法的电力设备故障区域分割方法[J]. 计算机应用与软件, 2021, 38(1): 222–226,237
WANG Jizheng, YIN Liju, XIAN Richang, et al. Power equipment fault area segmentation method based on improved slic algorithm[J]. Computer applications and software, 2021, 38(1): 222–226,237
[5] PRASAD S, SINGH V K, AKSHAY S. Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine[J]. International journal of computer applications, 2011, 8(12): 24–28.
[6] ZHAO Zhenbing, LIU Ning, WANG Le. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge[J]. IEEE transactions on dielectrics and electrical insulation, 2015, 22(6): 3421–3428.
[7] 伍锡如, 雪刚刚. 基于图像聚类的交通标志CNN快速识别算法[J]. 智能系统学报, 2019, 14(4): 670–678
WU Xiru, XUE Ganggang. CNN-based image clustering algorithm for fast recognition of traffic signs[J]. CAAI transactions on intelligent systems, 2019, 14(4): 670–678
[8] 吴涛, 王伟斌, 于力, 等. 轻量级YOLOV3的绝缘子缺陷检测方法[J]. 计算机工程, 2019, 45(8): 275–280
WU Tao, WANG Weibin, YU Li, et al. Insulator defect detection method for lightweight YOLOV3[J]. Computer engineering, 2019, 45(8): 275–280
[9] 王晓飞, 胡凡奎, 黄硕. 基于分布信息直觉模糊c均值聚类的红外图像分割算法[J]. 通信学报, 2020, 41(5): 120–129
WANG Xiaofei, HU Fankui, HUANG Shuo. Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering[J]. Journal on communications, 2020, 41(5): 120–129
[10] 曹长玉, 郑佳春, 黄一琦. 多模版匹配的特殊符号识别定位算法研究[J]. 计算机应用与软件, 2021, 38(3): 175–180,209
CAO Changyu, ZHENG Jiachun, HUANG Yiqi. A special symbol recognition and location algorithm based on multi-template matching[J]. Computer applications and software, 2021, 38(3): 175–180,209
[11] 刘靖钰, 刘德儿, 杨鹏, 等. 基于CNN网络的带遮挡车牌识别[J]. 测控技术, 2021, 40(2): 53–57
LIU Jingyu, LIU Deer, YANG Peng, et al. License plate recognition with partial occlusion based on CNN[J]. Measurement and control technology, 2021, 40(2): 53–57
[12] NAIEMI F, GHODS V, KHALESI H. An efficient character recognition method using enhanced HOG for Spam image detection[J]. Soft computing, 2019, 23(22): 11759–11774.
[13] KAUR S, SAGAR B B. Brahmi character recognition based on SVM (support vector machine) classifier using image gradient features[J]. Journal of discrete mathematical sciences and cryptography, 2019, 22(8): 1365–1381.
[14] HUANG Zhong, REN Fuji. Facial expression recognition based on multi-regional D-S evidences theory fusion[J]. IEEJ transactions on electrical and electronic engineering, 2016, 12(2): 251–261.
[15] CUI Zhichao, LIU Yuehu, REN Fuji. Homography-based traffic sign localisation and pose estimation from image sequence[J]. IET image processing, 2020, 13(14): 2829–2839.
[16] 马岭, 鲁越, 蒋慧琴, 等. 基于小样本学习的LCD产品缺陷自动检测方法[J]. 智能系统学报, 2020, 15(3): 560–567
MA Ling, LU Yue, JIANG Huiqin, et al. An automatic small sample learning-based detection method for LCD product defects[J]. CAAI transactions on intelligent systems, 2020, 15(3): 560–567
[17] REN Fuji, LIU Wenjie, WU Guoqing. Feature reuse residual networks for insect pest recognition[J]. IEEE access, 2019, 7: 122758–122768.
[18] LIN Ying, QIN Jiafeng, ZHANG Weiwei, et al. PCANet based digital recognition for electrical equipment infrared images[J]. Journal of physics:conference series, 2018, 1098: 012033.
[19] BINIZ M, EL AYACHI R. Recognition of tifinagh characters using optimized convolutional neural network[J]. Sensing and imaging, 2021, 22(1): 1–14.
[20] TORRES-GALVáN J C, GUEVARA E, KOLOSOVAS-MACHUCA E S, et al. Deep convolutional neural networks for classifying breast cancer using infrared thermography[J]. Quantitative InfraRed thermography journal, 2021: 1–12.
[21] 傅博, 姜勇, 王洪光, 等. 输电线路巡检图像智能诊断系统[J]. 智能系统学报, 2016, 11(1): 70–77
FU Bo, JIANG Yong, WANG Hongguang, et al. Intelligent diagnosis system for patrol check images of power transmission lines[J]. CAAI transactions on intelligent systems, 2016, 11(1): 70–77
[22] 黄锐勇, 戴美胜, 郑跃斌, 等. 电力设备红外图像缺陷检测[J]. 中国电力, 2021, 54(2): 147–155
HUANG Ruiyong, DAI Meisheng, ZHENG Yuebin, et al. Defect detection of power equipment by infrared image[J]. Electric power, 2021, 54(2): 147–155
[23] ZHAO Zhenbing, ZHEN Zhen, ZHANG Lei, et al. Insulator detection method in inspection image based on improved faster R-CNN[J]. Energies, 2019, 12(7): 1204.
[24] 施兢业, 刘俊. 基于改进区域生长法的电力设备红外图像分割[J]. 光学技术, 2017, 43(4): 381–384
SHI Jingye, LIU Jun. Mentation based on modified region growing algorithm[J]. Optical technique, 2017, 43(4): 381–384
[25] 张龙, 乔铁柱. 一种红外图像的二值化分割算法研究[J]. 红外技术, 2014, 36(8): 649–651
ZHANG Long, QIAO Tiezhu. An binary segmentation algorithm for infrared image[J]. Infrared technology, 2014, 36(8): 649–651
[26] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE, 2005: 886–893.
[27] 李欢. 基于稀疏表示的签名真伪鉴别方法研究[D]. 西安: 西安科技大学, 2017.
LI Huan. Off-line signature verification system based on sparse representation[D]. Xi’an: Xi’an University of Science and Technology, 2017.
[28] 郝惠敏, 梁永国, 武海彬, 等. 对称点模式-深度卷积神经网络的红外光谱识别方法[J]. 光谱学与光谱分析, 2021, 41(3): 782–788
HAO Huimin, LIANG Yongguo, WU Haibin, et al. Infrared spectrum recognition method based on symmetrized dot patterns coupled with deep convolutional neural network[J]. Spectroscopy and spectral analysis, 2021, 41(3): 782–788
- 相似文献/References:
-
[1]王凯旋,任福继,倪红军,等.基于循环互相关系数的CGAN温度值图像扩增[J].智能系统学报,2022,17(1):32.[doi:10.11992/tis.202106036]
WANG Kaixuan,REN Fuji,NI Hongjun,et al.Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN[J].CAAI Transactions on Intelligent Systems,2022,17():32.[doi:10.11992/tis.202106036]
备注/Memo
收稿日期:2021-05-27。
基金项目:江苏高校优势学科建设工程项目（PAPD）；德岛大学研究集群项目（2003002）
作者简介:王凯旋，硕士研究生，主要研究方向为电力设备缺陷检测和图像处理;任福继，教授，博士，日本工程院和欧盟科学院院士，中国人工智能学会名誉副理事长，日本工学会、IEICE、CAAI Fellow，日本国际先进信息研究所主席，主要研究方向为人工智能、情感计算、自然语言理解、模式识别。获吴文俊人工智能科学技术奖创新一等奖等。申请发明专利10余项，发表学术论文500余篇;倪红军，教授，博士，南通大学张謇学院院长，江苏高校优势学科带头人之一，再生铸造铝合金工程研究中心主任，中国有色金属协会再生金属分会学术委员会委员，中国再生资源产业技术创新战略联盟理事，主要研究方向为新能源新材料及装备技术、人工智能等。申请发明专利70余项，获授权37项，成功转让发明专利14项。发表学术论文70余篇
通讯作者:倪红军.E-mail：ni.hj@ntu.edu.cn
更新日期/Last Update:
1900-01-01