A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition

Zhang, Yuming; Chen, Cong; Wu, Qiong; Lü, Qi; Zhang, Su; Zhang, Guohui; Yang, Yin

doi:10.1109/tits.2018.2791476

Cited by 56 publications

(25 citation statements)

References 39 publications

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“…Zhang et al utilized the Microsoft Kinect to reconstruct pavement surfaces and capture geometric features of pavement cracking, including crack width, length, and depth to identify the distress severities of three major types of pavement cracks, namely, alligator cracking, traverse cracking, longitudinal cracking [100]. In the work of [101], Li et al employed laser-imaging techniques to model the pavement surface with dense 3D points and used an algorithm based on frequency analysis (Fourier transformation) separate potential cracks from the control profile and material texture of the pavement assuming that the road pavement in the absence of pavement distresses commonly holds a relatively uniform control profile.…”

Section: ) Traditional Methods For 3d Crack Detectionmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

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Section: ) Traditional Methods For 3d Crack Detectionmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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“…Furthermore, the mirror lens has a relatively long focal length of 500 mm, allowing to capture highresolution crack images from a farther distance compared to that demonstrated in a previous study. 36 Crack identification based on sensor fusion With the camera system described in the previous section, a crack identification strategy based on a sensor fusion algorithm is proposed. The main objective is to accurately measure crack information regardless of the angle of view by combining the commercial RGB-D and high-resolution digital cameras using a sensor fusion algorithm.…”

Section: Camera System For Crack Information Acquisitionmentioning

confidence: 99%

“…35 An RGB-D camera has been used for detecting relatively small damages such as cracks. Zhang et al 36 conducted an accuracy test on an artificial pavement crack with a width of 25.4 mm measured from a distance of 609.6 mm with 5% error. Although the potential of using an RGB-D camera for crack detection was demonstrated, their study only considered large cracks, which can be easily recognized using an RGB-D camera.…”

Section: Introductionmentioning

confidence: 99%

Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion

Kim

Lee

Ahn

et al. 2020

Structural Health Monitoring

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Cracks on concrete structures are an important indicator for assessing concrete durability and structural safety. Although such cracks are typically monitored by manual visual inspection, this method has drawbacks in terms of inspection time, safety, cost-effectiveness, and measurement accuracy. An innovative alternative is digital image processing, which can be used to obtain crack information from images captured using a digital camera. However, in image-based crack detection, the crack width may vary depending on the angle of the camera with respect to the concrete surface. A skewed angle of view is often encountered, particularly when capturing images from unmanned aerial vehicles or from higher locations. This study proposes a crack identification strategy using a combination of RGB-D and high-resolution digital cameras to accurately measure cracks regardless of the angle of view. The camera system is equipped with a tailored sensor fusion algorithm for crack identification, enabling a high measurement resolution and a robust depth estimation considering the skewed angle problem. An approximate plane corresponding to the concrete surface is introduced to effectively handle the high noise in the depth measurement data of the RGB-D camera. Subsequently, the crack image captured using the high-resolution digital camera is mapped onto the obtained plane model, allowing the crack width to be determined using the three-dimensional coordinates of each crack pixel. The measurement accuracy of the proposed approach is experimentally validated on an actual concrete structure.

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“…Research by input data and data collection. [2, Image [35][36][37] Radar [38][39][40][41][42][43][44][45][46] 3D images or point clouds [47,48] Acoustic…”

Section: Literature Reviewmentioning

confidence: 99%

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.

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A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition

Cited by 56 publications

References 39 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

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