Non-Invasive Coating Surface Defect Detection Through Visual Assessment and Multimodal Validation

This study introduces a modular approach to the validation of non-invasive coating inspection. It involves RGB imaging, visible and near-infrared spectroscopy, measurements of the physical parameters of the coating, and 3D Gaussian Splatting. As publicly available datasets containing these different types of information for the same coating samples are rare, each module was considered separately. The study also defines the type of paired data required for future data-fusion experiments. During the analysis of the painted-metal image dataset, exact-file hashing identified 68 duplicate images shared between the training and test sets. After removing these images and the duplicates within the test set, the best fixed 48×48 image classifier achieved an accuracy of 78.49% and a balanced accuracy of 78.99% on 93 distinct test images. In addition, an additional sensitivity analysis was conducted using an ImageNet-pretrained EfficientNetB0 model with 224×224 images. This model achieved an accuracy of 66.67%. This result shows that higher-resolution images and a pretrained model do not directly improve performance on a small and domain-specific dataset. Coated-wood defect localization, controlled steel-defect detector comparison, duplicate-grouped ship-coating spectroscopy, public gloss, roughness, scanning-electron-microscopy workbooks, and reflective-scene reconstruction were recomputed as separate validation blocks. The reflective-scene reconstruction achieved a peak signal-to-noise ratio of 25.018dB, a structural similarity index of 0.8947, and a learned perceptual image patch similarity value of 0.1522. However, camera distortion and unidirectional geometry limitations restrict this module to feasibility analysis. Our framework provides reproducible baselines and defines the paired-data requirements for future calibrated coating-monitoring systems.