A new image-quality evaluating and enhancing methodology for bridge inspection using an unmanned aerial vehicle

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL
H. Jung, Jin-Hwan Lee, Sungsik Yoon, Byunghyun Kim, Gi-Hun Gwon, In‐Ho Kim
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引用次数: 8

Abstract

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).
一种新的无人机桥梁检测图像质量评价与提高方法
本文提出了一种新的方法来解决随着无人机在桥梁检测领域的使用增加而遇到的图像质量问题。在对桥梁进行检测时,无人机所获得的图像会受到振动、风、无人机运动等各种干扰因素的影响。图像质量下降,如模糊、噪声和低分辨率是利用基于无人机的桥梁检测技术的主要障碍。特别是在桥梁检查领域,必须根据无人机获得的数据准确快速地检测损伤,这些质量问题削弱了使用无人机的优势,因为需要通过重新飞行重新拍摄图像。因此,本研究提出了基于局部模糊映射(LBM)的图像质量评估(IQA)和基于变分Dirichlet (VD)核估计的图像质量增强(IQE)作为解决质量问题的方法。首先,通过对桥梁各构件设置不同的相机参数采集图像数据。其次,通过离散小波变换(DWT)生成模糊图,并提出了一种新的模糊程度度量标准;第三,对模糊程度较大的低质量图像进行盲核估计和盲图像反卷积,提高图像质量。在验证试验中,将所提出的质量度量应用于无人机拍摄的桥墩和桥面材料图像集,并与基于奇异值分解(SVD)、灰度强度方差和(SGV)和高频多尺度融合与排序变换(HiFST)方法的质量度量结果进行了比较。通过与人类感知分类结果的比较,验证了所提出的IQA度量在无人机桥梁检测图像上具有更好的分类性能。此外,通过执行IQE,平均减少了26%的模糊,并且通过深度学习模型(即掩模和基于区域的卷积神经网络),增强质量的图像显示出更好的损伤检测性能。
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来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
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