Metal corrosion can cause many problems, how to quickly and effectively assess the grade of metal corrosion and timely remediation is a very important issue. Typically, this is done by trained surveyors at great cost. Assisting them in the inspection process by computer vision and artificial intelligence would decrease the inspection cost. In this paper, we propose a dataset of metal surface correction used for computer vision detection and present a comparison between standard computer vision techniques by using OpenCV and deep learning method for automatic metal surface corrosion grade estimation from single image on this dataset. The test has been performed by classifying images and calculating the accuracy for the two different approaches.
Classical photometric stereo requires uniform collimated light, but point light sources are usually employed in practical setups. This introduces errors to the recovered surface shape. We found that when the light sources are evenly placed around the object with the same slant angle, the main component of the errors is the low-frequency deformation, which can be approximately described by a quadratic function. We proposed a postprocessing method to correct the deviation caused by the nonuniform illumination. The method refines the surface shape with prior information from calibration using a flat plane or the object itself. And we further introduce an optimization scheme to improve the reconstruction accuracy when the three-dimensional information of some locations is available. Experiments were conducted using surfaces captured with our device and those from a public dataset. The results demonstrate the effectiveness of the proposed approach.
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