A comparison of learning-based approaches for the corrosion detection on barrels in industrial applications

Abstract Machine-learning-based (ML) segmentation in the image domain can be utilized for the detection of corrosion on the surface of industrial objects. This research provides a comparison of techniques using convolutional neural networks (CNNs) on the one hand, and random forest (RF) classifiers within RGB and HSV feature spaces on the other hand. CNN-based approaches usually need a large amount of data for training in order for the network to converge and generalize well on new data. Due to the low amount of data provided, we apply a set of methods to increase the generalization ability of the model. These methods can be categorized into data augmentation, selection of larger and smaller models and pretraining strategies like self supervised learning (SSL). The RF classifiers on the other hand are trained per pixel, so that the amount of data is determined by the image size. The object to be tested is a barrel made of metal, from which the image of the coat is used as the training data, and the image of the bottom as test data. We found that a RF classifier in the RGB feature space outperforms the CNNs by seven percentage points regarding the f 1-score of the corrosion class.