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Drainage pipeline defect detection method based on attention mechanism

A technology for defect detection and drainage pipes, applied in neural learning methods, computer components, image data processing, etc., can solve problems such as unsatisfactory results and no use of unique features of pipe data

Pending Publication Date: 2020-02-21
TIANJIN UNIV
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Problems solved by technology

However, none of these methods take advantage of the unique characteristics of the pipeline data, and the effect is not ideal.

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  • Drainage pipeline defect detection method based on attention mechanism
  • Drainage pipeline defect detection method based on attention mechanism
  • Drainage pipeline defect detection method based on attention mechanism

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Embodiment Construction

[0046] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and examples. Apparently, the described implementation is only a part of the embodiments of the present invention, rather than an exhaustive list of all the embodiments. And in the case of no conflict, the implementations in this description and the features in the embodiments can be combined with each other.

[0047] The processing steps of the present invention include: data preparation and processing, establishment of a training set, a verification set, and a test set, training a convolutional neural network, using a trained network model to automatically classify defects, and the like.

[0048] Step 1: Data preparation and processing. The python program is used to extract the defect pictures inside the pipeline from the obtained drainage pipe defect detection report, a...

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Abstract

The invention relates to a drainage pipeline defect detection method based on an attention mechanism. The method comprises the following steps: extracting a defect picture from a pipeline defect detection report, and classifying the defect picture into seven types including deformation, corrosion, scaling, misalignment, deposition, leakage and rupture according to the corresponding defect type; building an attention mechanism module, wherein the adopted attention mechanism module is a CBAM module; building a neural network model based on an attention mechanism, removing the last three convolution layers of the VGG16 network in the convolution network, and adding a plurality of CBAM modules on the basis; training the neural network by using a back propagation algorithm, verifying in the training process, and storing the optimal model of network training when the verification accuracy is the highest; and testing the test set by using the stored optimal model to obtain a classification result of the pipeline defect pictures.

Description

technical field [0001] The aspects involved in the present invention include computer vision, computer image processing, deep learning and other computer fields and the field of abnormal detection of drainage pipes. The present invention is more focused on the application of deep learning technology to the detection of drainage pipeline defects. Background technique [0002] The sewer system is one of the largest pieces of infrastructure in a city, designed to collect and transport wastewater and stormwater. The normal use of the system is very important for the safety of urban drainage. With the rapid development of cities in recent years, my country has highlighted the problems of insufficient construction scale of underground pipelines and low management level. Events such as torrential rain and road collapse occurred one after another in some cities, seriously affecting people's life and urban operation order. Therefore, regular inspection and repair of drainage pipes...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q50/26G06T7/00
CPCG06N3/084G06Q50/26G06T7/0002G06N3/045G06F18/2431
Inventor 潘刚穆罕默德·乌马尔·泽山郑耀先孙迪
Owner TIANJIN UNIV
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