Quantitative Assessments of Crack Sealing Benefits by 3D Laser Technology

Yang, Zhongyu; Zhang, Xinyi; Tsai, Yichang; Wang, Zhaohua

doi:10.1177/03611981211027897

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Ghosh et al [73] employed YOLO and Faster R-CNN to detect cracks in range images collected by the 3D imaging system. Yang et al [74] utilized 3D laser technology to measure the growth of crack lengths when they are sealed and non-sealed to quantify the crack sealing benefit. Li et al [28] proposed a CNN framework to classify range images into transverse cracks, longitudinal cracks, block cracks, and alligator cracks.…”

Section: D Image Data In Pavementmentioning

confidence: 99%

Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images

Eslami

Yun

2022

Journal of Advanced Transportation

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Automated recognition of road surface objects is vital for efficient and reliable road condition assessment. Despite recent advances in developing computer vision algorithms, it is still challenging to analyze road images due to the low contrast, background noises, object diversity, and variety of lighting conditions. Motivated by the need for an improved pavement objects classification, we present Dual Attention Convolutional Neural Network (DACNN) to improve the performance of multiclass classification using intensity and range images collected with 3D laser imaging devices. DACNN fuses heterogeneous information in intensity and range images to enhance distinguishing foreground from background, as well as to improve object classification in noisy images under various illumination conditions. DACNN also leverages multiscale input images by capturing contextual information for object classification with different sizes and shapes. DACNN contains an attention mechanism that (i) considers semantic interdependencies in spatial and channel dimensions and (ii) adaptively fuses scale-specific and mode-specific features so that each feature has its own level of contribution to the final decision. As a practical engineering project, dataset are collected from road surfaces using 3D laser imaging. DACNN is compared with four deep classifiers that are widely used in transportation applications. Experiments show that DACNN consistently outperforms the baselines by 22–35% on average in terms of the F-score. A comprehensive discussion is also presented regarding computational costs and how robustly the investigated classifiers perform on each road object.

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Section: D Image Data In Pavementmentioning

confidence: 99%

Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images

Eslami

Yun

2022

Journal of Advanced Transportation

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“…In a similar way, Zhang et al used PCA (Principal Component Analysis) to calculate the elevation of small segments of points along individual scanning lines and generate a control profile, analysing saliency of points in order to detect the cracks. Other solution to detect cracks in 3D laserimages is the generation of crack probability maps obtained by path voting [16] or minimal path algorithm [17], [18]. MLS (Mobile Laser Scanner) systems combine LiDAR sensors with navigation-positioning units to cover large areas of road quickly while acquiring comprehensive and accurate data of the road surface and their surroundings in the form of georeferenced 3D point clouds.…”

Section: Introductionmentioning

confidence: 99%

Identification of Relevant Point Cloud Geometric Features for the Detection of Pavement Cracks Using MLS Data

Río-Barral

Grandío

Riveiro

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The maintenance of road infrastructures is one of the main challenges that transportation authorities must face to guarantee the safe mobility of people and goods. Novel remote monitoring technologies offer advanced solutions for this issue, allowing the inspection of large sections of the network in a time-effective way. In this paper, we introduce a methodology for the detection of cracks on road pavements using point clouds acquired with a mobile laser scanner. First, the points of the cloud are labelled as pavement or cracks based on field annotations, and local geometric features of the points are calculated using principal component analysis. Two different machine learning classifiers, Support Vector Machine (SVM) and Random Forest, are then trained to identify crack points using the point feature data. The crack points predicted by the classifiers are clustered as individual instances and compared to the corresponding ones from a test dataset. Although pointwise performance of the method is modest, it can correctly identify and measure areas of the pavement affected by cracking.

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Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds

et al. 2022

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Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.

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Quantitative Assessments of Crack Sealing Benefits by 3D Laser Technology

Cited by 3 publications

References 22 publications

Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images

Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images

Identification of Relevant Point Cloud Geometric Features for the Detection of Pavement Cracks Using MLS Data

Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds

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