Oil and gas pipeline marker identification method based on neural network

An oil and gas pipeline, neural network technology, applied in biological neural network model, scene recognition, neural architecture and other directions, can solve problems such as large interference, and achieve the effect of eliminating interference information, fast speed, and small model

Active Publication Date: 2020-06-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] In pipeline safety management, it is more hoped that the aerial pictures can be detected in real time to determine whether there is any abnormality; for the aerial pictures, the landmarks are relatively small compared to the whole background, and due to the complex and varie

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  • Oil and gas pipeline marker identification method based on neural network
  • Oil and gas pipeline marker identification method based on neural network
  • Oil and gas pipeline marker identification method based on neural network

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[0029] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0030] Such as figure 1Shown is the target detection network structure model diagram based on the neural network construction of the present invention, that is, the Mobilenet-SSD network structure diagram embedded with CBAM. The input is a picture with a size of 300×300×3, the first layer is a standard convolution layer, represented by Conv, and the convolution with a step size of 2 is performed by 32 3×3×3 convolution kernels, and the output is 150× 150×32 feature map; the next 13 layers represent 13 depthwise separable convolution modules, represented by Depthwise; the depthwise separable convolution module of the second layer is first performed by a 3×3×32 convolution kernel with a step size of The Depthwise convolution of 1 outputs a feature map of 150×150×32, and then 64 convolution kernels of ...

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Abstract

The invention provides an oil and gas pipeline marker identification method based on a neural network. The method comprises the steps of constructing a model, obtaining a training set, a verificationset and a test set, obtaining an optimal model through training, performing testing by adopting the test set, obtaining a detection result and calculating detection precision. According to the method,the Mobilenet with few parameters is used as a basic network, the SSD algorithm directly regressing on multi-scale feature mapping is used as a target detection algorithm, multi-scale target detection is achieved, a detection model is small, the detection speed is high, the detection model is deployed at a mobile terminal, and markers of the oil and gas pipeline are detected in real time; the significant characteristics of the marker are highlighted from the global and local ranges through a CBAM attention mechanism, and a better detection effect is obtained; and meanwhile, the attention mechanism is embedded behind each layer of the basic network, so that the feature expression capability can be enhanced layer by layer from the first layer, interference information is effectively eliminated, and the detection precision is improved.

Description

technical field [0001] The invention belongs to the field of oil and gas pipeline safety, in particular to a neural network-based identification method for oil and gas pipeline markers. Background technique [0002] Oil, natural gas and other resources play an important strategic role, and their transportation has the characteristics of long distance and wide range. Due to the wear, corrosion and other factors of the oil and gas pipeline system itself, leakage occurs from time to time, and oil and gas leakage can easily cause explosions, fires, and toxic and harmful Therefore, it is of great significance to strengthen the safety management of oil and gas pipelines. [0003] In the field of oil and gas pipeline safety, in addition to time-consuming and labor-intensive manual inspections, there are currently methods for pipeline leak detection based on neural network training pipeline weld defect data or pressure wave signals when pipeline leaks. However, changes in the surro...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/13G06V2201/09G06V2201/07G06N3/045G06F18/214
Inventor 于永斌唐倩彭辰辉陆瑞军买峰汤亦凡戚敏惠邓权芯毛宇涵
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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