A Two-Stage Deep Learning Framework for Automated Corrosion Detection and Severity Estimation in High-Resolution SEM Images

Accurate detection and severity estimation of corrosion on metallic surfaces is essential for maintaining material integrity and ensuring operational safety in industrial systems. To address limitations in manual inspection methods, this study presents a two-stage deep learning pipeline tailored for high-resolution scanning electron microscopy images. The framework combines instance-level corrosion segmentation using the YOLOv8-seg architecture with subsequent severity classification performed by EfficientNet-B0 and ResNet18. In the segmentation stage, models are trained using both manually annotated and automatically generated binary masks, enabling robust instance mask prediction through prototype-based mask decoding. The classification stage assesses the severity of corrosion by analyzing localized regions based on morphological features, leveraging convolutional neural networks optimized for binary output. Experimental results demonstrate strong performance: the segmentation model trained on manual annotations achieves a mean Intersection-over-Union (mIoU) of 89.91, a mask mAP@50 of 98.6, and an ROC-AUC of 94.69. For severity classification, EfficientNet-B0 achieves an accuracy of 93.75% and an F1-score of 93.29, outperforming ResNet18. These findings underscore the potential of combining lightweight segmentation and classification models to enable precise and automated corrosion analysis, supporting integration into real-time inspection workflows and intelligent material diagnostics in industrial settings.