Accuracy assessment of semantic segmentation for automatic aesthetic control on composite components

This work focuses on the effect of operational and environmental parameters on the image pre-processing step for automatic aesthetic control of composite components by a vision system. The image pre-processing provides first an evaluation of the their quality and a selection of the best ones, then the semantic segmentation of the parts of interest in the images, for an integrated and effective procedure, to improve the accuracy of the following inspection phase. With the aim of defining image selection criteria, a set of photos of the components under analysis are corrupted according increasing levels of blur, contrast degradation and noise. Experimental correlation between image quality indicators and segmentation performance metrics has been found. The training dataset size and the class balance appeared as the most important parameters for semantic segmentation performances. The analysis, carried out on carbon fibre components of complex geometry and superficial appearance, seems promising for automatic inspection.