Damage detection on steel-reinforced concrete produced by corrosion via YOLOv3: A detailed guide

Abstract

Damage assessment applied to reinforced concrete elements is one of the main activities of infrastructure maintenance tasks. Among these elements, the problem of corrosion in reinforced concrete is particularly critical and requires careful consideration. Annually, governments invest a large amount of economic resources in this activity. However, most methodologies for damage assessment rely on visual inspection, which may be subjectively interpreted, producing inconsistent results and requiring a considerable amount of time and resources. This study evaluates the performance of real-time object detection using You Only Look Once, version 3, for detecting corrosion damage in concrete structures. The architecture of YOLOv3 is based on a complex, but efficient, convolutional neural network fed by a dataset proposed and labeled by the authors. Two training stages were established to improve the model precision, using transfer learning with medium- and high-resolution training images. The test results show satisfactory concrete-corrosion detection through validation photographs and videos demonstrating the capabilities of explainable artificial intelligence and its applications in civil engineering.

A One-Step Methodology for Identifying Concrete Pathologies Using Neural Networks—Using YOLO v8 and Dataset Review

“…Guzmán-Torres et al [6] emphasize the fact that one of the main activities of infrastructure maintenance is to assess damage to reinforced concrete elements. Corrosion is one of the pathologies that deserves the most attention, and maintenance activity requires major government investment in this area.…”

Vessel Surface Corrosion Segmentation Approach Using a Decision-Tree Module for YOLO Trained Models

“…Corrosion as isolated area units on a vessel surface is difficult to both detect and predict in RGB images, with a diverse geometrical shape, which makes it difficult to postulate prior knowledge on the basis of a generalised morphology in terms of image processing. Recent advances in deep learning models [5,6] have examined the applicability and potential of machine vision for the inspection of large structures [7][8][9][10] and segmentation of corrosion in marine vessels [3,[11][12][13].…”

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

“…This is mainly due to the diverse geometrical shape, which makes it difficult to postulate prior knowledge on the basis of a generalised morphology. Recent advances in deep learning models [16,17] have examined the applicability and potential of machine vision for the inspection of large structures [18][19][20] and segmentation of corrosion in marine vessels [2,11,[21][22][23].…”