  In the realm of industrial manufacturing, product inspection remains a
significant bottleneck, with only a small fraction of manufactured items
undergoing inspection for surface defects. Advances in imaging systems and AI
can allow automated full inspection of manufactured surfaces. However, even the
most contemporary imaging and machine learning methods perform poorly for
detecting defects in images with highly textured backgrounds, that stem from
diverse manufacturing processes. This paper introduces an approach based on
graph Fourier analysis to automatically identify defective images, as well as
crucial graph Fourier coefficients that inform the defects in images amidst
highly textured backgrounds. The approach capitalizes on the ability of graph
representations to capture the complex dynamics inherent in high-dimensional
data, preserving crucial locality properties in a lower dimensional space. A
convolutional neural network model (1D-CNN) was trained with the coefficients
of the graph Fourier transform of the images as the input to identify, with
classification accuracy of 99.4%, if the image contains a defect. An
explainable AI method using SHAP (SHapley Additive exPlanations) was used to
further analyze the trained 1D-CNN model to discern important spectral
coefficients for each image. This approach sheds light on the crucial
contribution of low-frequency graph eigen waveforms to precisely localize
surface defects in images, thereby advancing the realization of zero-defect
manufacturing.
