scholarly journals A CAPSNETS APPROACH TO PAVEMENT CRACK DETECTION USING MOBILE LASER SCANNNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 39-44
Author(s):  
W. Zhu ◽  
W. Tan ◽  
L. Ma ◽  
D. Zhang ◽  
J. Li ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Traffic Accidents ◽  
Crack Detection ◽  
Point Clouds ◽  
Huge Amount ◽  
Processing Efficiency ◽  
The Road ◽  
Dense Point ◽  
Intensity Images ◽  
Pavement Crack Detection

Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.

scholarly journals Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4198
Author(s):  
Mianqing Zhong ◽  
Lichun Sui ◽  
Zhihua Wang ◽  
Dongming Hu
Keyword(s):  
Laser Scanning ◽  
Crack Detection ◽  
Point Clouds ◽  
Acquisition Time ◽  
Shape Parameters ◽  
Regular Grid ◽  
Scanning Angle ◽  
Average Accuracy ◽  
Length And Area ◽  
Pavement Crack Detection

This paper presents a novel algorithm for detecting pavement cracks from mobile laser scanning (MLS) data. The algorithm losslessly transforms MLS data into a regular grid structure to adopt the proven image-based methods of crack extraction. To address the problem of lacking topology, this study assigns a two-dimensional index for each laser point depending on its scanning angle or acquisition time. Next, crack candidates are identified by integrating the differential intensity and height changes from their neighbors. Then, morphology filtering, a thinning algorithm, and the Freeman codes serve for the extraction of the edge and skeleton of the crack curves. Further than the other studies, this work quantitatively evaluates crack shape parameters: crack direction, width, length, and area, from the extracted crack points. The F1 scores of the quantity of the transverse, longitudinal, and oblique cracks correctly extracted from the test data reached 96.55%, 87.09%, and 81.48%, respectively. In addition, the average accuracy of the crack width and length exceeded 0.812 and 0.897. Experimental results demonstrate that the proposed approach is robust for detecting pavement cracks in a complex road surface status. The proposed method is also promising in serving the extraction of other on-road objects.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress

2020 ◽  
Vol 12 (6) ◽  
pp. 942 ◽  
Author(s):  
Maria Rosaria De Blasiis ◽  
Alessandro Di Benedetto ◽  
Margherita Fiani
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Road Traffic ◽  
Single Point ◽  
Three Dimensional ◽  
Point Clouds ◽  
Road Surface ◽  
Important Indicator ◽  
The Road ◽  
Laser Systems

The surface conditions of road pavements, including the occurrence and severity of distresses present on the surface, are an important indicator of pavement performance. Periodic monitoring and condition assessment is an essential requirement for the safety of vehicles moving on that road and the wellbeing of people. The traditional characterization of the different types of distress often involves complex activities, sometimes inefficient and risky, as they interfere with road traffic. The mobile laser systems (MLS) are now widely used to acquire detailed information about the road surface in terms of a three-dimensional point cloud. Despite its increasing use, there are still no standards for the acquisition and processing of the data collected. The aim of our work was to develop a procedure for processing the data acquired by MLS, in order to identify the localized degradations that mostly affect safety. We have studied the data flow and implemented several processing algorithms to identify and quantify a few types of distresses, namely potholes and swells/shoves, starting from very dense point clouds. We have implemented data processing in four steps: (i) editing of the point cloud to extract only the points belonging to the road surface, (ii) determination of the road roughness as deviation in height of every single point of the cloud with respect to the modeled road surface, (iii) segmentation of the distress (iv) computation of the main geometric parameters of the distress in order to classify it by severity levels. The results obtained by the proposed methodology are promising. The procedures implemented have made it possible to correctly segmented and identify the types of distress to be analyzed, in accordance with the on-site inspections. The tests carried out have shown that the choice of the values of some parameters to give as input to the software is not trivial: the choice of some of them is based on considerations related to the nature of the data, for others, it derives from the distress to be segmented. Due to the different possible configurations of the various distresses it is better to choose these parameters according to the boundary conditions and not to impose default values. The test involved a 100-m long urban road segment, the surface of which was measured with an MLS installed on a vehicle that traveled the road at 10 km/h.

scholarly journals Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Weidong Song ◽  
Guohui Jia ◽  
Hong Zhu ◽  
Di Jia ◽  
Lin Gao
Keyword(s):  
High Performance ◽  
Crack Detection ◽  
Automated Detection ◽  
Key Factors ◽  
The Road ◽  
Intelligent Maintenance ◽  
Road Pavement ◽  
Maintenance Systems ◽  
High Level ◽  
Pavement Crack Detection

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

scholarly journals Automatic Road Pavement Assessment with Image Processing: Review and Comparison

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Sylvie Chambon ◽  
Jean-Marc Moliard
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
State Of The Art ◽  
Civil Infrastructures ◽  
The Road ◽  
Multi Scale ◽  
Road Pavement ◽  
Optical Images ◽  
Detection Approach ◽  
Pavement Crack Detection

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

scholarly journals PEDESTRIAN DETECTION BY LASER SCANNING AND DEPTH IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 465-468
Author(s):  
A. Barsi ◽  
T. Lovas ◽  
B. Molnar ◽  
A. Somogyi ◽  
Z. Igazvolgyi
Keyword(s):  
Laser Scanning ◽  
Pedestrian Detection ◽  
Laser Scanner ◽  
Point Clouds ◽  
Shopping Mall ◽  
Complex Flow ◽  
Video Footage ◽  
Flow Parameters ◽  
Dense Point ◽  
Commercial Applications

Pedestrian flow is much less regulated and controlled compared to vehicle traffic. Estimating flow parameters would support many safety, security or commercial applications. Current paper discusses a method that enables acquiring information on pedestrian movements without disturbing and changing their motion. Profile laser scanner and depth camera have been applied to capture the geometry of the moving people as time series. Procedures have been developed to derive complex flow parameters, such as count, volume, walking direction and velocity from laser scanned point clouds. Since no images are captured from the faces of pedestrians, no privacy issues raised. The paper includes accuracy analysis of the estimated parameters based on video footage as reference. Due to the dense point clouds, detailed geometry analysis has been conducted to obtain the height and shoulder width of pedestrians and to detect whether luggage has been carried or not. The derived parameters support safety (e.g. detecting critical pedestrian density in mass events), security (e.g. detecting prohibited baggage in endangered areas) and commercial applications (e.g. counting pedestrians at all entrances/exits of a shopping mall).

scholarly journals ONLY IMAGE BASED FOR THE 3D METRIC SURVEY OF GOTHIC STRUCTURES BY USING FRAME CAMERAS AND PANORAMIC CAMERAS

2016 ◽  
Vol XLI-B5 ◽  
pp. 363-370 ◽  
Author(s):  
A. Pérez Ramos ◽  
G. Robleda Prieto
Keyword(s):  
Laser Scanning ◽  
Imaging Techniques ◽  
Laser Scanner ◽  
Point Clouds ◽  
Office Work ◽  
Light Conditions ◽  
External Quality ◽  
Panoramic Cameras ◽  
Dense Point ◽  
Close Range

Indoor Gothic apse provides a complex environment for virtualization using imaging techniques due to its light conditions and architecture. Light entering throw large windows in combination with the apse shape makes difficult to find proper conditions to photo capture for reconstruction purposes. Thus, documentation techniques based on images are usually replaced by scanning techniques inside churches. Nevertheless, the need to use Terrestrial Laser Scanning (TLS) for indoor virtualization means a significant increase in the final surveying cost. So, in most cases, scanning techniques are used to generate dense point clouds. However, many Terrestrial Laser Scanner (TLS) internal cameras are not able to provide colour images or cannot reach the image quality that can be obtained using an external camera. Therefore, external quality images are often used to build high resolution textures of these models. This paper aims to solve the problem posted by virtualizing indoor Gothic churches, making that task more affordable using exclusively techniques base on images. It reviews a previous proposed methodology using a DSRL camera with 18-135 lens commonly used for close range photogrammetry and add another one using a HDR 360° camera with four lenses that makes the task easier and faster in comparison with the previous one. Fieldwork and office-work are simplified. The proposed methodology provides photographs in such a good conditions for building point clouds and textured meshes. Furthermore, the same imaging resources can be used to generate more deliverables without extra time consuming in the field, for instance, immersive virtual tours. In order to verify the usefulness of the method, it has been decided to apply it to the apse since it is considered one of the most complex elements of Gothic churches and it could be extended to the whole building.

scholarly journals TERRESTRIAL PHOTOGRAMMETRY VS LASER SCANNING FOR RAPID EARTHQUAKE DAMAGE ASSESSMENT

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 527-533 ◽  
Author(s):  
C. Vasilakos ◽  
S. Chatzistamatis ◽  
O. Roussou ◽  
N. Soulakellis
Keyword(s):  
Structure From Motion ◽  
Damage Assessment ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Earthquake Damage ◽  
Map Building ◽  
Dense Point ◽  
Terrestrial Photogrammetry ◽  
Earthquake Damage Assessment

<p><strong>Abstract.</strong> Building damage assessment caused by earthquakes is essential during the response phase following a catastrophic event. Modern techniques include terrestrial and aerial photogrammetry based on Structure from Motion algorithm and Laser Scanning with the latter to prove its superiority in accuracy assessment due to the high-density point clouds. However, standardized procedures during emergency surveys often could not be followed due to restrictions of outdoor operations because of debris or decrepit buildings, the high human presence of civil protection agencies, expedited deployment of survey team and cost of operations. The aim of this paper is to evaluate whether terrestrial photogrammetry based on a handheld amateur DSLR camera can be used to map building damages, structural deformations and facade production in an accepted accuracy comparing to laser scanning technique. The study area is the Vrisa village, Lesvos, Greece where a Mw&amp;thinsp;6.3 earthquake occurred on June 12th, 2017. A dense point cloud from some digital images created based on Structure from Motion algorithm and compared with a dense point cloud acquired by a laser scanner. The distance measurement and the comparison were conducted with the Multiscale Model to Model Cloud Comparison method. According to the results, the mean of the absolute distances between the two clouds is 0.038&amp;thinsp;m while the 94.9&amp;thinsp;% of the point distances are less than 0.1&amp;thinsp;m. Terrestrial photogrammetry proved to be an accurate methodology for rapid earthquake damage assessment thus its products were used by local authorities for the calculation of the compensation for the property loss.</p>

scholarly journals INVESTIGATIONS INTO THE QUALITY OF IMAGE-BASED POINT CLOUDS FROM UAV IMAGERY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 539-545 ◽  
Author(s):  
H.-J. Przybilla ◽  
M. Lindstaedt ◽  
T. Kersten
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Image Data ◽  
Point Clouds ◽  
Data Sets ◽  
Test Field ◽  
Data Format ◽  
Software Packages ◽  
Dense Point

<p><strong>Abstract.</strong> The quality of image-based point clouds generated from images of UAV aerial flights is subject to various influencing factors. In addition to the performance of the sensor used (a digital camera), the image data format (e.g. TIF or JPG) is another important quality parameter. At the UAV test field at the former Zollern colliery (Dortmund, Germany), set up by Bochum University of Applied Sciences, a medium-format camera from Phase One (IXU 1000) was used to capture UAV image data in RAW format. This investigation aims at evaluating the influence of the image data format on point clouds generated by a Dense Image Matching process. Furthermore, the effects of different data filters, which are part of the evaluation programs, were considered. The processing was carried out with two software packages from Agisoft and Pix4D on the basis of both generated TIF or JPG data sets. The point clouds generated are the basis for the investigation presented in this contribution. Point cloud comparisons with reference data from terrestrial laser scanning were performed on selected test areas representing object-typical surfaces (with varying surface structures). In addition to these area-based comparisons, selected linear objects (profiles) were evaluated between the different data sets. Furthermore, height point deviations from the dense point clouds were determined using check points. Differences in the results generated through the two software packages used could be detected. The reasons for these differences are filtering settings used for the generation of dense point clouds. It can also be assumed that there are differences in the algorithms for point cloud generation which are implemented in the two software packages. The slightly compressed JPG image data used for the point cloud generation did not show any significant changes in the quality of the examined point clouds compared to the uncompressed TIF data sets.</p>

scholarly journals Fusion of range-based data and image-based datasets for efficient documentation of cultural heritage objects and sites

2015 ◽  
Vol XL-5/W7 ◽  
pp. 277-281 ◽  
Author(s):  
J. L. Lerma ◽  
M. Cabrelles ◽  
S. Navarro
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Indirect Methods ◽  
Recording Project ◽  
Dense Point ◽  
The Times ◽  
Cultural Heritage Objects ◽  
Scanning Systems ◽  
Direct And Indirect Methods

Nowadays it is possible to measure accurately dense point clouds either with aerial/terrestrial laser scanning systems or with imagebased solutions (namely based on photogrammetric computer vision algorithms such as structure-from-motion (SfM)), from which highly detailed 3D models can be achieved. Besides, direct tools in the form of simple devices such as rulers, compass and plumblines are usually required in simple metric surveys, as well as high-end surveying and geodetic instruments such as robotized imagebased total stations and GNSS (probably to a lesser degree but still required) to set the archaeological/architectural recording project in a global reference frame. With all this gamut of image-based and range-based sensors and datasets (in the form of coordinates, point clouds or 3D models), in different coordinate systems (most of the times local for each device), lack of uniform scale, orientation and levelling, the fusion of data tends to be cumbersome. This paper presents an efficient way to fuse and merge different datasets in the form of point clouds/3D models and geodetic/UTM coordinates. The new developed 3DVEM – Register GEO software is able to handle datasets coming from both direct and indirect methods in order to provide unified and precise deliverables.

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