The purpose of this paper is to explore the use of fully convolutional neural networks (FCN) to perform a semantic segmentation of deflectometric recordings for quality control of reflective surfaces. The proposed method relies on a U-net network to identify the location and boundaries of the object and the possible defective areas present on it by performing a pixel-wise classification based on local curvatures and data modulation. Experiments were performed on a real industrial problem using four variations of the architecture. The results demonstrate that the method combining geometric and photometric information enables the identification of a wider variety of shape and texture imperfections, with the resulting segmentations closely correlated with the visual impact of the defects. In addition, several suggestions are presented for near-term industrial utilization. |
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CITATIONS
Cited by 5 scholarly publications.
Image segmentation
Inspection
Cameras
Modulation
Data modeling
Computer programming
Image processing