<![CDATA[Corrosion inspection of industrial assets is still dominated by subjective and inconsistent visual inspections. This study develops and validates a deep learning-based corrosion area detection system on metal surfaces in the context of heavy equipment through a binary segmentation task (corrosion vs. non-corrosion). Three architectures were compared: UNet, VGG16–Random Forest, and VGG16–UNet, using 600 annotated images measuring 512 × 512 pixels taken under lighting conditions of 50–150 lux. The workflow included preprocessing, augmentation, training for 30, 50, and 100 epochs, and evaluation of accuracy, precision, recall, IoU/Jaccard, Dice, and confusion matrix per pixel (positive = corrosion). The results show that VGG16–UNet provides the best performance; in the 150 lux test, it achieved 98.96% accuracy, 0.9934 precision, and 0.994 recall, with good consistency across lighting variations and data scales. These findings confirm the effectiveness of a pre-trained encoder combined with skip connections to recover fine corrosion boundaries and produce reliable corrosion maps. The proposed approach has the potential to standardize the inspection process and accelerate decision-making in reliability-based maintenance practices.]]>
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