A deep learning-based approach for automatic detection of concrete cracks below the waterline

Abstract

Convolutional neural networks have been created as deep learning-based approaches to automatically analyze photographs of concrete surfaces for crack diagnosis applications. Although deep learning-based systems assert to have extremely high accuracy, they frequently overlook how difficult it is to acquire images. Complex lighting situations, shadows, the irrationality of crack forms and widths, imperfections, and concrete spall frequently have an influence on real-world photos. The focus of the published research and accessible shadow databases is on photographs shot in controlled laboratory settings. In this research, we investigate the challenging underwater optical effects settings and the complexity of image classification for concrete crack detection. This research elaborates on difficulties encountered when using deep learning-based techniques to identify concrete cracks when optical effects are present. To improve the precision of automatically detecting concrete cracks on underwater surfaces, new optical effect augmentation techniques have been developed.

Uncertainty Quantification of Neural Reflectance Fields for Underwater Scenes

“…The numerical experiments demonstrated that our ensemble model can explicitly infer uncertainty in the model on both synthetic and real scenes, thereby exhibiting superior performance and outperforming previous works in key metrics related to reconstruction error and rendering quality. The proposed algorithm will benefit the downstream tasks of ocean exploration and navigation, such as the automatic identification of damage in underwater infrastructure [18,19], target detection and tracking, mapping and motion planning, etc. The present work is not without limitations.…”

“…Unlike WaterNeRF, SeaThru-NeRF [17] introduces a scattering image formation model to capture the impact of the underwater medium on imaging by respectively assigning color and density parameters to objects and the medium, to model the effects of shallow water natural ambient light. Additionally, a typical physicalbased reconstruction of the optical scene in shallow underwater environment conditions is discussed in [18,19]. They predicted the impact of optical effects on underwater images by inputting the underwater images into an underwater wave propagation model.…”

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

“…Visual inspection and dye penetrant testing are the traditional methods for crack detection, but they are time-consuming, expensive, and not always accurate. It has already been demonstrated by the works presented in [1,59] that machine learning approaches offer a very promising solution for detecting cracks in underwater concrete structures.…”

“…These models can be developed using wave action or motion systems, which are also known as phase averaging or phase resolving models. The latest generation of phase average models is commonly used in various applications, including commercial tools for flow simulation and wave modeling in advanced professional engineering [59]. In this paper, we have used the model presented in [59] to generate the 3D wave geometry for image augmentation based on the JONSWAP spectrum, which has been proven to be more suitable for a fetch-limited sea with increasing waves [60].…”

“…In this study, it is important to have realistic images of underwater concrete structures, so the technique for rendering illumination effects is presented in [59]. First, it is assumed that the surface of the water is level, and the sun's refraction vector is detected.…”