Multi-target Detection and Fault Recognition Image Processing Method

被引：0
|
作者：
Bai J. [1 ]
Zhao R. [1 ]
Gu F. [2 ]
Wang J. [2 ]
机构：
[1] Nanrui Group Co., Ltd., Beijing
[2] Beijing Kedong Electric Power Control System Co., Ltd., Beijing
来源：
Gaodianya Jishu/High Voltage Engineering | 2019年 / 45卷 / 11期
关键词：
Data mining; Deep learning; Defect detection; Faster RCNN; Multi-objects; Positioning and identification;
D O I：
10.13336/j.1003-6520.hve.20191031014
中图分类号：
学科分类号：
摘要：
In order to use the deep learning to achieve multi-target recognition of transmission lines and multiple fault detection, adopting the "faster regions with convolutional neural networks features" (Faster RCNN) network as an algorithm framework, we mined data of drone images, and proposed three improvement strategies for 6 target detection tasks of transmission lines, namely the adaptive image preprocessing algorithm, the area-based non-maximum suppression algorithm, and the segmentation detection scheme. The research results show that the improved algorithm in this paper can accurately locate and identify faults by use of mined data and realize multi-target fault detection in aerial images under complex backgrounds, and can be analogized to other similar multi-target application scenarios. The research can prove a reference for the detection and recognition of muti-target. © 2019, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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收藏
页码：3504 / 3511
页数：7
相关论文
共 29 条
  • [1] Peng X., Liang F., Qian J., Et al., Automatic recognition of insulator from UAV infrared image based on periodic textural feature, High Voltage Engineering, 45, 3, pp. 922-928, (2019)
  • [2] Liu Z., Du Y., Chen Y., Et al., Simulation and experiment on the safety distance of typical ±500 kV DC transmission lines and towers for UAV inspection, High Voltage Engineering, 45, 2, pp. 426-432, (2019)
  • [3] Huang X., Wang Y., Zhu Y., Et al., Icing forecast of transmission line based on genetic algorithm and fuzzy logic, High Voltage Engineering, 42, 4, pp. 1228-1235, (2016)
  • [4] Deng H., Lu Q., Chen L., Et al., Detection for composite insulator based on ultrasonic guided wave, High Voltage Engineering, 42, 4, pp. 1236-1244, (2016)
  • [5] Sun J., Research on Diagnosis of Insulator Crack Based on Edge Detection, (2008)
  • [6] Zhang F., Identification and Research of Abnormal Patrol Diagram of Transmission Line Based on Computer Vision, (2015)
  • [7] Zhao J., Zhang L., Research on multiple feature extraction techniques of insulator images, Electrical Measurement & Instrumentation, 576, 50, pp. 37-41, (2013)
  • [8] Dai J., Song H., Sheng G., Et al., Prediction method for power transformer running state based on LSTM network, High Voltage Engineering, 44, 4, pp. 1099-1106, (2018)
  • [9] Tong W., Research on transmission line identification and condition monitoring method based on aerial image, (2011)
  • [10] Tong W., Yu H., Zhang Q., Insulator fault diagnosis based on neural network, Computer Simulation, 30, 9, pp. 310-313, (2013)
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