Unsupervised domain adaptation for crack detection

Weng, Xingxing; Huang, Yuchun; Li, Yanan; Yang, He; Yu, Shixin

doi:10.1016/j.autcon.2023.104939

Cited by 28 publications

(6 citation statements)

References 23 publications

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“…Ref. [279] delves into domain adaptation methods for computer vision, showcasing instance-based, feature-based, decision-based, generative, and meta-learning methods, while addressing challenges and future directions. For recent advances in visual recognition, ref.…”

Section: Paper Contribution Advantagesmentioning

confidence: 99%

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Tanveer,

Fatima,

Zardari

et al. 2023

Applied Sciences

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This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.

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Section: Paper Contribution Advantagesmentioning

confidence: 99%

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Tanveer,

Fatima,

Zardari

et al. 2023

Applied Sciences

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“…Some have focused on domain adaptation-based image generation, addressing shifts in materials, imaging conditions, and environmental factors. For instance, Weng et al [29] conducted various domain adaptation tasks to generate images in new environments using crack damage images. They employed DACrack, an unsupervised-learning-based domain adaptation model, to detect anomalies in new domain data.…”

Section: Generation Of Structural Damage Imagesmentioning

confidence: 99%

Image-to-Image Translation-Based Structural Damage Data Augmentation for Infrastructure Inspection Using Unmanned Aerial Vehicle

Gwon,

Lee,

Kim

et al. 2023

Drones

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As technology advances, the use of unmanned aerial vehicles (UAVs) and image sensors for structural monitoring and diagnostics is becoming increasingly critical. This approach enables the efficient inspection and assessment of structural conditions. Furthermore, the integration of deep learning techniques has been proven to be highly effective in detecting damage from structural images, as demonstrated in our study. To enable effective learning by deep learning models, a substantial volume of data is crucial, but collecting appropriate instances of structural damage from real-world scenarios poses challenges and demands specialized knowledge, as well as significant time and resources for labeling. In this study, we propose a methodology that utilizes a generative adversarial network (GAN) for image-to-image translation, with the objective of generating synthetic structural damage data to augment the dataset. Initially, a GAN-based image generation model was trained using paired datasets. When provided with a mask image, this model generated an RGB image based on the annotations. The subsequent step generated domain-specific mask images, a critical task that improved the data augmentation process. These mask images were designed based on prior knowledge to suit the specific characteristics and requirements of the structural damage dataset. These generated masks were then used by the GAN model to produce new RGB image data incorporating various types of damage. In the experimental validation conducted across the three datasets to assess the image generation for data augmentation, our results demonstrated that the generated images closely resembled actual images while effectively conveying information about the newly introduced damage. Furthermore, the experimental validation of damage detection with augmented data entailed a comparative analysis between the performance achieved solely with the original dataset and that attained with the incorporation of additional augmented data. The results for damage detection consistently demonstrated that the utilization of augmented data enhanced performance when compared to relying solely on the original images.

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“…Cracks pose a significant risk to the safety and health of structures such as bridges, dams, pavements, walls, and tunnels. If not detected promptly, they can cause irreversible and extensive damage 1 . Therefore, in the construction and maintenance of modern buildings, crack detection plays a crucial role 2 .…”

Section: Introductionmentioning

confidence: 99%

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Nie,

Liu,

Zhang

2024

Sci Rep

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Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

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Unsupervised domain adaptation for crack detection

Cited by 28 publications

References 23 publications

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Image-to-Image Translation-Based Structural Damage Data Augmentation for Infrastructure Inspection Using Unmanned Aerial Vehicle

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

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