Surface Corrosion Grade Classification using Convolution Neural Network
Sanjay Kumar Ahuja1, Manoj Kumar Shukla2, Kiran Kumar Ravulakollu3

1Sanjay Kumar Ahuja, AIIT, Amity University, Noida, India.
2Manoj Kumar Shukla, ASET, Amity University, Noida, India.
3Kiran Kumar Ravulakollu, University of Petroleum & Energy Studies (UPES), Dehradun, India.

Manuscript received on 20 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 7645-7649 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6196098319/2019©BEIESP | DOI: 10.35940/ijrte.C6196.098319

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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.
Keywords: Corrosion Detection, Image Processing, Convolution Neural Network, Mask RCNN

Scope of the Article: Classification