Self-encoding neural network-based wind turbine visual detection system

A neural network and visual detection technology, applied in biological neural network models, neural architectures, wind turbines, etc., can solve the problem of few artificial neural networks, and achieve the effects of low cost, improved reliability and accuracy, and high efficiency

Inactive Publication Date: 2018-01-26
NANTONG UNIVERSITY
View PDF5 Cites 23 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In recent years, scholars at home and abroad have carried out a lot of research work on wind turbine identification, mainly using acoustic emission technolo...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Self-encoding neural network-based wind turbine visual detection system
  • Self-encoding neural network-based wind turbine visual detection system
  • Self-encoding neural network-based wind turbine visual detection system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] Considering the safety and reliability of wind turbine detection, the present invention mainly uses unmanned aerial vehicle technology and computer technology as the means to realize wind turbine identification. In order to ensure a high detection accuracy, the pre-training of the positive and negative sample sets is introduced before the BP neural network training, and the feature vectors that are more representative of the sample set are extracted. fan area.

[0032] Such as figure 1 As shown, the present invention designs a fan visual detection method based on self-encoding neural network, realizes the fan detection system based on UAV monitoring aerial photography and neural network, and preprocesses some frames of fan images in the UAV aerial video, Compose the training sample set and test sample set for wind turbine vision detection, construct the self-encoder neural network, put the positive and negative samples in the training set into the self-encoder for pre-...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention discloses a self-encoding neural network-based wind turbine visual detection system. According to the system, a part of frames of wind turbine images in an unmanned aerial vehicle aerial video are pre-processed, and the pre-processed images constitute a training sample set and a test sample set for wind turbine visual detection; a self-encoding neural network is built; positive and negative samples in the training sample set are arranged in a self-encoder so as to be pre-trained; an activation quantity which is obtained through calculation is adopted to replace original input, so that a new sample feature set is formed; new feature vectors are inputted to an established BP neural network, so that the two-pass classifier of a wind turbine can be obtained; the sample images in the test set are arranged in the classifier so as to be tested; areas containing the wind turbine are searched through using a random sliding window method; and screening is performed throughnon-maximum suppression, so that a final wind turbine detection area can be obtained. The system of the invention can be widely applied to fields such as pattern recognition and intelligent inspectionof wind power generation.

Description

technical field [0001] The invention relates to a fan visual detection system. Background technique [0002] The area where wind turbines are usually located is tidal flats or offshore, with wide distribution, complex working conditions, and harsh working environment. It is difficult to ensure the efficiency and safety of staff only by means of manual inspection. In addition, the installed capacity of wind turbines is expanding year by year. , which will undoubtedly increase the difficulty of condition monitoring and optimal maintenance of wind turbines. Therefore, it is necessary to use drone technology to monitor the operating status of wind turbines by aerial photography, and use computer technology to realize visual inspection of wind turbines and improve the reliability of wind turbine detection. In recent years, scholars at home and abroad have carried out a lot of research work on wind turbine identification, mainly using acoustic emission technology and infrared the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00G06K9/66G06K9/00G06N3/04G06N3/06G06N3/08F03D17/00
Inventor 徐一鸣张娟顾菊平陆观刘成成徐星华亮陈峰朱建红
Owner NANTONG UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap