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

Abstract

This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks’ performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

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.…”

Localization of Cracks in Concrete Structures Lacking Reference Objects and Feature Points Using an Unmanned Aerial Vehicle

“…Maslan et al [13] developed an automatic detection and evaluation system for runway surface cracks using UAVs and Deep-CNN. Orinait ė et al [14] conducted research on the use of machine learning for the detection of underwater concrete cracks and verified the efficiency and accuracy of the proposed approach. Liu et al [15] conducted research on the detection of concrete cracks based on computer vision using U-Net and found it to be more robust and effective compared to CNN-based methods.…”