Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

Abstract

Vessel maintenance requires periodic visual inspection of the hull in order to detect typical defective situations of steel structures such as, among others, coating breakdown and corrosion. These inspections are typically performed by well-trained surveyors at great cost because of the need for providing access means (e.g., scaffolding and/or cherry pickers) that allow the inspector to be at arm’s reach from the structure under inspection. This paper describes a defect detection approach comprising a micro-aerial vehicle which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access, and a coating breakdown/corrosion detector based on a three-layer feed-forward artificial neural network. As it is discussed in the paper, the success of the inspection process depends not only on the defect detection software but also on a number of assistance functions provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects of the work are described along the different sections of the paper, as well as the classification performance attained.

On the use of robots and vision technologies for the inspection of vessels: A survey on recent advances

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Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN

“…Five statistical projection features were extracted from the detection area of the surface image, and were used by the extreme learning machine (ELM) and region of background (ROB) pre-detection classifiers. A coating damage/corrosion detection device based on a three-layer feedforward artificial neural network was introduced by Reference [9]. Krummenacher et al [10] designed an artificial neural 2 of 16 network with constant cyclic movement to detect wheel deviation and roundness error, and they simulated the relationship between the inherent measurement values of these defects.…”

Visual Inspection of the Aircraft Surface Using a Teleoperated Reconfigurable Climbing Robot and Enhanced Deep Learning Technique

“…In the last decade, various visual inspection algorithms have been applied to the field of aircraft inspection and surface defect detection. These visual inspection algorithms are classified into two types, such as traditional image processing-based visual inspection [5,[23][24][25] and machine learning techniques [26][27][28][29][30][31]. Typically, the traditional algorithms use rudimentary characteristics like edges, brightness, histogram, and spectral feature to detect and segment defects [28].…”