A pipeline video defect detection method based on a convolutional neural network

A convolutional neural network and defect detection technology, applied in neural learning methods, biological neural network models, neural architectures, etc. Automatic detection efficiency, great application value, and the effect of improving accuracy

Inactive Publication Date: 2019-04-02
NEW TECH APPL INST BEIJING CITY +1
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Problems solved by technology

However, this method is not very versatile, and the generalization ability of image classification is not strong. When there are too many image

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  • A pipeline video defect detection method based on a convolutional neural network
  • A pipeline video defect detection method based on a convolutional neural network
  • A pipeline video defect detection method based on a convolutional neural network

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[0031] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0032] A pipeline video defect detection method based on convolutional neural network, by sampling video frames, training multiple binary classification convolutional neural networks to detect and classify each frame of images, counting the results returned by each convolutional neural network, and determining The defect type of the video frame, and finally count the results of the entire vid...

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Abstract

The invention relates to a pipeline video defect detection method based on a convolutional neural network. The method comprises the steps of extracting frames of a video, training a plurality of CNNsto classify each frame of image; counting the result returned by each CNN, determining the defect type of the frame, segmenting the video into continuous image frames by taking the pipeline closed circuit television video as input, and sending each frame of image into a plurality of trained CNNNs for binary classification, wherein the classification result only comprises a certain specific defectand no defect. According to the invention, the accuracy of pipeline defect detection is obviously improved; a feasible method is provided for video detection; the automatic detection efficiency of thepipeline defects can be improved; the pipeline defect detection method has the advantages that the detection accuracy is high, the detection speed is high, the application value in pipeline video defect detection is high, a satisfactory result is obtained, and the pipeline defect detection method can be used as a technical reference for pipeline defect detection workers and can well meet the requirements of practical application.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a pipeline video defect detection method based on a convolutional neural network. Background technique [0002] Drainage pipes are an important part of the urban drainage system. During use, the functional and structural defects of the pipes caused by environmental factors lead to the failure of the drainage pipes to work normally. When the rainstorm hits, the rainwater cannot be discharged in time. The situation of "waterlogging into the sea" has brought inconvenience to urban construction and people's lives. In order to maximize the drainage capacity of existing pipelines, regular inspection of existing drainage pipelines is an effective measure to discover hidden dangers of drainage pipelines in time. [0003] At present, CCTV (Closed Circuit Television) detection technology is widely used in the internal inspection of pipelines. Pipeline robots carry cam...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06T2207/20081G06T2207/20084G06T2207/10016G06F18/24
Inventor 刘克会吕学强王艳霞李程徐栋
Owner NEW TECH APPL INST BEIJING CITY
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