Pipeline heating diagnosis method of unmanned aerial vehicle infrared video for crude oil pipeline inspection

Abstract

The invention provides a pipeline heating diagnosis method of an unmanned aerial vehicle infrared video for crude oil pipeline inspection. The pipeline heating diagnosis method comprises the followingsteps: step 1, performing shooting to obtain a video picture of a pipeline by utilizing infrared equipment carried by an unmanned aerial vehicle; step 2, splicing the images according to a matching method of a neural network; 3, performing inter-frame difference according to the spliced images; 4, extracting the main direction of the long-distance pipeline from the processed image; 5, extractinguseful long-distance pipeline information from the infrared image by taking the extracted main direction as priori knowledge; and step 6, identifying and judging faults and damages existing in the long-distance pipeline along the obtained main direction of the pipeline area according to a pipeline abnormality judgment criterion. According to the pipeline heating diagnosis method of the unmanned aerial vehicle infrared video for crude oil pipeline inspection, a high-quality image can be obtained, and automatic positioning and diagnosis of an abnormal heating crude oil pipeline based on the infrared video are realized.

Description

technical field [0001] The present invention relates to the technical field of unmanned aerial vehicles for inspection of long-distance crude oil pipelines, in particular to a method for diagnosing pipeline fever using infrared video of unmanned aerial vehicles for inspections of crude oil pipelines. Background technique [0002] In order to meet the daily maintenance and inspection of large-area crude oil pipelines, modern and automated technical means are urgently needed. UAV inspection with infrared equipment is an ideal solution. [0003] Infrared images show the thermal radiation emitted from the surface of the subject. Compared with visible light images, the contrast between the target and the background in infrared images is relatively low, and the edge contours of objects are relatively blurred, which brings difficulties to conventional edge extraction. In addition, the infrared video captured by the infrared gimbal equipped with the drone also contains a lot of ran...

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Problems solved by technology

[0003] Infrared images show the thermal radiation emitted from the surface of the subject. Compared with visible light images, the contrast between the target and the background in infrared images is relatively low, and the edge contours of objects are relatively blurred, which brings difficulties to conventional edge extraction.

In addition, the infrared video captured by the infrared gimbal equipped with the drone also contains a lot of random noise, and the background image will be relatively complex. It is difficult to automatically obtain pipeline information from such infrared images. It is more difficult to automatically identify and diagnose abnormally hot crude oil pipelines

In addition, affected by electronic thermal noise and signal transmission interference, salt and pepper noise, pulse noise and other types of noise may appear in the infrared image, resulting in the infrared image of the crude oil pipeline being interfered by blurred edges, poor contrast, environmental noise pollution, etc.

[0004] Yang Zhengbo et al. described a gradient method-based detection method for pipeline infrared temperature anomalies in the literature "Transmission Line Fault Diagnosis Based on Infrared Image Recognition". Based on the brightness, hue and saturation characteristics of infrared images, after Noise reduction processing, using gradient descent to identify abnormal temperature parts, so as to find possible problems. The disadvantage of this method is that it is very sensitive to background images and cannot achieve good results in complex environments.

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