Automatic assessment of rust level on screws using convolutional neural networks

ABSTRACT:

This paper presents a deep learning-based approach to automatically classify the rust level of screws using ResNet-18 and MobileNetV3 convolutional neural networks. A controlled salt-spray chamber was used to simulate corrosion on metal screws over 0h, 48h, 96h, and 168h of exposure. Images were processed with a circle-detection algorithm to extract individual screws, followed by data augmentation and training. The final models achieved a classification accuracy greater than 94% on the validation set.