A Study of Computational Vision Methods for Corrosion Detection in Industrial Assets - Marco, Anderson Gonçalves; Oliveira, Marcos de Souza; Prates, Rodrigo Leite; Lopes, Murilo Cruz; Casal, Rogério Guedes; Guimarães, Cristiani Vilela Ribeiro (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2025.38322, 2025)
Corrosion has a significant impact on the global economy, leading to high costs for preventing damage, repairing deterioration, and replacing defective components. Industry 4.0 brings computational vision as a key role for visual inspection of assets ensuring quality and reducing the downtime. This study conducts a comparative analysis of computer vision techniques for detecting corrosion, with an emphasis on segmentation neural networks built upon U-Net architectures, focusing on the trade-off between segmentation accuracy and computational efficiency. We benchmark four U-Net-based segmentation models using pretrained backbones: MobileNetV2, ResNet152, InceptionV4 and VGG16 on a publicly available data set of corroded metallic surfaces. All models are trained using the full dataset and evaluated based on per-image F1-score and training time. Our results show that InceptionV4 achieves the highest F1-score (0.895), while MobileNetV2 offers a comparable score (0.892) with nearly half the training time. These findings suggest that lightweight backbones can deliver accurate segmentation with significantly reduced computational cost, making them promising candidates for near real-time industrial inspection systems under resource constraints.