Oil and gas pipeline marker identification method based on neural network

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...

AI Technical Summary

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 varied terrain of the scene, the interference is large, and with the aerial photography There are also frequent scale changes in highly variable markers; therefore, the main challenge at present is how to use smaller and faster models to identify multi-scale targets and smaller targets

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