|Inspection engineering is a highly important field in the Oil & Gas sector for analysing the health of offshore assets. Corrosion, a naturally occurring phe-nomenon, arises as a result of a chemical reaction between a metal and its envi-ronment, causing it to degrade over time. Costing the global economy an esti-mated US $2.5 Trillion per annum, the destructive nature of corrosion is evi-dent. Following the downturn endured by the industry in recent times, the need to combat corrosion is escalated, as companies look to cut costs by increasing efficiency of operations without compromising critical processes. This paper presents a step towards automating solutions for real-time inspection using state-of-the-art computer vision and deep learning techniques. Experiments con-cluded that there is potential for the application of computer vision in the in-spection domain. In particular, Mask R-CNN applied on the original images (i.e. without any form of pre-processing) was found to be most viable solution, with the results showing a mAP of 77.1%.|
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