Deep neural networks for external corrosion classification in industrial above-ground storage tanks

This paper uses transfer learning and fine-tuning approaches to explore EfficientNet smaller models for a multiclassification task of corrosion types in above-ground storage tanks. Data augmentation was used to increment the data an oil and gas company provided, reaching a dataset of around 5000 images. The images were stored in Google Drive and imported into Colab to train the models using TensorFlow and Keras. After the hyperparameters' tuning a transfer learning model was selected and explored with fine-tuning. The EfficientNetB0 model delivered from fine-tuning accomplished 94 % performance. This work is the first attempt to deploy an artificial vision automatic tool for being implemented during tank inspection in the industrial sector. In further development, this model can be coupled with one based on object detection to remotely identify failures due to external corrosion issues, improving safety and reliability in oil and gas operations.