Automated corrosion surface quantification in steel transmission towers using UAV photogrammetry and deep convolutional neural networks

Abstract

Corrosion in steel transmission towers poses a challenge to structural integrity and safety, requiring efficient detection methods. Traditional visual inspections are unsustainable due to the complexity and volume of structures. Their manual, qualitative, and subjective nature often leads to inconsistencies in maintenance planning. This study proposes a deep learning-based approach for semantic segmentation of corroded areas on steel towers. Using the DeepLabv3+ model, the network was trained and validated on 999 field photographs. MobileNetV2, serving as the feature extractor, was chosen for its optimal balance between accuracy and computational efficiency, achieving a validation accuracy of 90.8% and a loss of 0.23. The trained network was applied to real-world inspections using orthomosaics derived from photogrammetric reconstructions of the South-East tower at the Torino Eremo broadcasting center. These photogrammetric products not only enabled precise segmentation of corroded areas but also provided the foundation for corrosion quantification with metrical accuracy, a critical advantage for maintenance planning. Unlike traditional image segmentation methods, which lack a spatial reference and precise scaling, the photogrammetric approach ensures that the corrosion extent and distribution are quantified in exact physical dimensions, enhancing the reliability of the analysis. The results show that deep learning-based inspections can automate detection, providing reliable data and reducing reliance on manual inspections, enhancing efficiency, safety, and accuracy.