Segmentation and Recognition of Highway Assets Using Image-Based 3D Point Clouds and Semantic Texton Forests

Golparvar-Fard, Mani; Balali, Vahid; Garza, Jesús M. de la

doi:10.1061/(asce)cp.1943-5487.0000283

Cited by 64 publications

(34 citation statements)

References 36 publications

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“…where P u (y, e, v, x) and P d (y, e, v, x) denote the probabilities of the upper and lower layer, respectively, and these two probabilities can be factorized like Equation (19). With the above joint probability, several methods can be used to implement the maximum probabilistic inference.…”

Section: Unified Inference Model For Crf and Bnmentioning

confidence: 99%

“…Uhlmann et al [18] employ various visible color descriptors to represent SAR images and then perform supervised classification. In [19,20], Golparvar and Balali present the texton-based and non-parameter feature extraction methods for image segmentation, which are innovative and can also be considered for SAR images.…”

Section: Sar Images Classificationmentioning

confidence: 99%

“…With the assumption of the global Markov property in FG, the joint probability of each layer in semantic pyramid P (y, e, v, x) can be factorized by: P (y, e, v, x) = P (e, v|y, x) P (y, x) = P (v|e) P (e|y) P (y|x) P (x) (19) where P (v|e) and P (e|y) denote the causalities among edges, vertices and labels in BN, namely Equations (14) and (15), respectively; P (y|x) represents Equation (6), and P (x) is constant because x is observed.…”

Section: Unified Inference Model For Crf and Bnmentioning

confidence: 99%

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Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Fan

Feng

et al. 2017

Remote Sensing

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This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification.(1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents' regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy.

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Section: Unified Inference Model For Crf and Bnmentioning

confidence: 99%

Section: Sar Images Classificationmentioning

confidence: 99%

Section: Unified Inference Model For Crf and Bnmentioning

confidence: 99%

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Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Fan

Feng

et al. 2017

Remote Sensing

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“…Recent advancements in lower-cost optical cameras, image processing, and three-dimensional (3D) modeling have enabled photogrammetry to three-dimensionally reconstruct objects from digital images, for civil or transportation structure applications [16,[32][33][34][35][36]. Photogrammetry provides the ability to obtain quantitative measurements from 3D models created from quality easily documented two-dimensional (2D) images, and has been used for assessing the condition of transportation assets [21,37].…”

Section: Introductionmentioning

confidence: 99%

A Novel Application of Photogrammetry for Retaining Wall Assessment

2017

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Abstract:Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1-3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment.

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“…The first MMS were stabilized on cars [7] and subsequently have had numerous applications already implemented in road maintenance. MLS can be used to take stock and measure the position of road infrastructure elements [8][9][10]. Data obtained by terrestrial MMS can also be used to create a spatial database [11], to carry out road safety inspections [12,13], to model the road area [14], to detect voids and cracks in the pavement [8] and to detect road signs [15,16].…”

Section: Introductionmentioning

confidence: 99%

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Pastucha

2016

Remote Sensing

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This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures' presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified.

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Segmentation and Recognition of Highway Assets Using Image-Based 3D Point Clouds and Semantic Texton Forests

Cited by 64 publications

References 36 publications

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

A Novel Application of Photogrammetry for Retaining Wall Assessment

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

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