Kinect, a Novel Cutting Edge Tool in Pavement Data Collection

Abstract

ABSTRACT:Pavement roughness and surface distress detection is of interest of decision makers due to vehicle safety, user satisfaction, and cost saving. Data collection, as a core of pavement management systems, is required for these detections. There are two major types of data collection: traditional/manual data collection and automated/semi-automated data collection. This paper study different non-destructive tools in detecting cracks and potholes. For this purpose, automated data collection tools, which have been utilized recently are discussed and their applications are criticized. The main issue is the significant amount of money as a capital investment needed to buy the vehicle. The main scope of this paper is to study the approach and related tools that not only are cost-effective but also precise and accurate. The new sensor called Kinect has all of these specifications. It can capture both RGB images and depth which are of significant use in measuring cracks and potholes. This sensor is able to take image of surfaces with adequate resolution to detect cracks along with measurement of distance between sensor and obstacles in front of it which results in depth of defects. This technology has been very recently studied by few researchers in different fields of studies such as project management, biomedical engineering, etc. Pavement management has not paid enough attention to use of Kinect in monitoring and detecting distresses. This paper is aimed at providing a thorough literature review on usage of Kinect in pavement management and finally proposing the best approach which is cost-effective and precise.

Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor

“…The Microsoft Kinect One (Kinect V2) included an infrared laser emitter, an infrared ray absorber sensor, a Red-Green-Blue (RGB) sensor, a rotating motor on the base, and microphones to detect the external sound in different directions. The analytical features of Microsoft Kinect 1 and Microsoft Kinect Xbox 360 were presented in Mahmoudzadeh et al (2015) [28]. Microsoft Kinect Xbox 360 cannot capture images in all environments since it provides low quality or incomplete depth images under direct sunlight.…”

Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field

“…Moazzam et al [14] used Kinect sensor to capture depth images, generated meshes of the pothole, and calculated some geometrical information of the pothole like depth, area, and volume. Mahmoudzadeh et al [15] provided a thorough literature review on usage of Kinect in pavement management and proposed the best approach which is costeffective and precise. 3D reconstruction-based methods can obtain detailed information of the potholes.…”

“…Vibration-based methods [5][6][7][8] require small storage and can be used in realtime processing, but it can only get a rough assessment of pavement potholes or even provide wrong results. 3D reconstruction-based methods [9][10][11][12][13][14][15] can obtain detailed information of the potholes, but the drawback is that they cost too much equipment money or too much calculation time. 2D vision-based methods [16][17][18][19][20][21][22] can find a balance between the vibration-based methods and 3D reconstruction-based methods.…”

Transfer Learning Using Deep Neural Networks for Classification of Truck Body Types Based on Side-Fire Lidar Data

“…Although image/ video base models are accurate, in practice they face some performance limitations in low illumination and bad weather conditions. Hence, it is important to investigate alternative types of technologies such as 3D scanners to improve the performance of these monitoring systems which are popular tools for pavement studies in transportation (Chang et al 2005;Mahmoudzadeh et al 2015).…”