Pole Tip Corrosion Detection Using Various Image Processing Techniques

Yammen, Suchart; Bunchuen, Somjate; Boonsri, Ussadang; Muneesawang, Paisarn

doi:10.1007/978-1-4614-3501-3_35

Cited by 3 publications

(1 citation statement)

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“…In recent years, the image processing based corrosion detection and analysis approaches have been carried out for inspections in various fields, particularly where access is not viable owing to environmental and other variables engaged [1]. According to the survey [2] on solutions to corrosion based on computer vision, most of the methods are based on image characteristics.…”

Section: Related Workmentioning

confidence: 99%

Surface Corrosion Grade Classification using Convolution Neural Network

Ahuja¹,

Shukla²,

Ravulakollu³

2019

IJRTE

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 Abstract: Corrosion is a prevalent issue in the oil and gas industry. Usually, pipelines made of Iron are used for oil and gas transportation. The pipelines are large and distributed over big fields above the ground, underground and even underwater. Corrosion gets developed because of environmental variables such as temperature, humidity and acidic nature of the liquids. There are different techniques for detecting and monitoring corrosion development, both destructive and non-destructive. Visual inspection is a common technique of surface corrosion analysis, but manual inspection is extremely dependent on the inspecting person's abilities and expertise. The findings of the manual inspection are qualitative and may be biased, may result into the accidents because of incorrect analysis. Corrosion must be accurately detected in early phases to prevent unwanted accidents. This paper will present a computer vision-based approach in combination with deep learning for corrosion classification as perISO-8501 standard. The findings of the assessment are unbiased and in a fair acceptable range similar to the outcomes of the visual inspection.

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Section: Related Workmentioning

confidence: 99%