Automatic assessment of concrete cracks in low-light, overexposed, and blurred images restored using a generative AI approach

IF 9.6 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Automation in Construction Pub Date : 2024-09-21 DOI:10.1016/j.autcon.2024.105787
Pengwei Guo , Xiangjun Meng , Weina Meng , Yi Bao
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Abstract

Deep learning-based computer vision techniques have high efficiency in assessing concrete cracks from images, and the assessment can be automated using robots for higher efficiency. However, assessment accuracy is often compromised by low-quality images. This paper presents a Conditional Generative Adversarial Network (CGAN)-based approach to restore low-light, overexposed, and blurred images. The approach integrates attention mechanisms and residual learning and uses Wasserstein loss with gradient penalty. Crack assessment results show that the proposed approach outperforms state-of-the-art methods, regarding structural similarity (SSIM: 0.78 for deblurring, 0.95 for low-light enhancement, and 0.96 for overexposure correction) and peak signal-to-noise ratio (PSNR: 28.6 for deblurring, 31.4 for low-light enhancement, and 31.6 for overexposure correction). Restored images have been used to train a deep learning model for assessing concrete cracks. The Intersection over Union (IoU) and F1 score of crack segmentation are higher than 0.98 and 0.99, respectively, revealing high accuracy in crack assessment tasks.
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利用生成式人工智能方法自动评估低照度、曝光过度和模糊图像中的混凝土裂缝修复情况
基于深度学习的计算机视觉技术在从图像评估混凝土裂缝方面具有很高的效率,而且可以使用机器人自动进行评估,从而提高效率。然而,低质量的图像往往会影响评估的准确性。本文提出了一种基于条件生成对抗网络(CGAN)的方法,用于还原低照度、曝光过度和模糊的图像。该方法整合了注意力机制和残差学习,并使用带有梯度惩罚的 Wasserstein 损失。破解评估结果表明,所提出的方法在结构相似性(SSIM:去除模糊为 0.78,弱光增强为 0.95,曝光过度校正为 0.96)和峰值信噪比(PSNR:去除模糊为 28.6,弱光增强为 31.4,曝光过度校正为 31.6)方面优于最先进的方法。修复后的图像被用于训练评估混凝土裂缝的深度学习模型。裂缝分割的交集大于联合(IoU)和 F1 分数分别高于 0.98 和 0.99,显示了裂缝评估任务的高准确性。
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来源期刊
Automation in Construction
Automation in Construction 工程技术-工程:土木
CiteScore
19.20
自引率
16.50%
发文量
563
审稿时长
8.5 months
期刊介绍: Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities. The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.
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