Classification of mean corrosion depth for ordinary steel materials exposed to real bridges using transfer learning with multiple CNN models

Abstract

In countermeasures against infrastructure aging, such as steel bridges, the evaluation of corrosion on plain steel is a critical issue. Techniques that can rapidly assess corrosion severity from images contribute to more efficient maintenance and cost reduction. In this study, transfer learning was applied using several representative CNN models (VGG19, ResNet50, InceptionV3 and EfficientNetB3) to classify mean corrosion depth from steel corrosion images and their effectiveness was compared. Using a dataset of steel specimens exposed on an actual bridge, InceptionV3 notably achieved 86.7 percent accuracy on a four-level corrosion severity classification task. Although model performance varied and challenges remain in identifying early-stage corrosion, advanced corrosion could be classified with high accuracy. These findings provide foundational data for the development of an automated image-based corrosion assessment system.