Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km2 in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs

Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed. In this paper, a method is presented to tackle the problems related to the data including obstruction, clutter and noise. This method reconstructs indoor space in a model-driven approach using watertight predefined models. Employing the two-step implementation of this process, the algorithm is able to model non-rectangular spaces with an even number of sides. Afterwards, an “improvement” process increases the level of details by modeling the intrusion and protrusion of the model. The 3D model is formed by extrusion from 2D to 3D. The proposed model-driven algorithm is evaluated with four benchmark real-world datasets. The efficacy of the proposed method is proved by the range of [77%, 95%], [85%, 97%] and [1.7 cm, 2.4 cm] values of completeness, correctness and geometric accuracy, respectively

DeepWindows: Windows Instance Segmentation through an Improved Mask R-CNN Using Spatial Attention and Relation Modules

Windows, as key components of building facades, have received increasing attention in facade parsing. Convolutional neural networks have shown promising results in window extraction. Most existing methods segment a facade into semantic categories and subsequently employ regularization based on the structure of manmade architectures. These methods merely concern the optimization of individual windows, without considering the spatial areas or relationships of windows. This paper presents a novel windows instance segmentation method based on Mask R-CNN architecture. The method features a spatial attention region proposal network and a relation module-enhanced head network. First, an attention module is introduced in the region proposal network to generate a spatial attention map, then the attention map is multiplied with the objectness scores of the classification branch. Second, for the head network, relation modules are added to model the spatial relationships between proposals. Appearance and geometric features are combined for instance recognition. Furthermore, we constructed a new window instance segmentation dataset with 1200 annotated images. With our dataset, the average precisions of our method on detection and segmentation increased from 53.1% and 53.7% to 56.4% and 56.7% compared with Mask R-CNN. A comparison with state-of-the-art methods also proves the predominance of our proposed method

The Prevalence of Impacted Third Molar, Impaction Angulation, and Impaction Depth in Patients Visiting Dental Clinics and Private Offices in Ghaemshahr, Iran, in 2016

Background & Aims:  The present study aimed to determine the prevalence of impacted third molar as well as impaction angulation and depth. Materials & Methods: In this cross-sectional descriptive study, 261 panoramic radiographs belonging to patients visiting dental clinics and offices in Ghaemshahr, Iran, were evaluated and the presence of impacted wisdom teeth was examined. Moreover, the angulation of impacted teeth, impaction depth, and the relationship of the tooth to the mandibular ramus were recorded. The data were recorded, collected, and statistically analyzed in SPSS 22 using the non-parametric chi-square test. p <0.05 was considered significant. Results: Of the 261 patients entering the study, 52 (17.69%) had at least one impacted tooth. Of the total number of patients with impacted teeth, 31 were women (mean prevalence of 19.87% of the total population of women) and 21 were men (15.22% of the total population of men), showing no significant difference (p=0.29). In terms of impaction depth based on Pell and Gregory’ classification, Class C impaction depth was the most prevalent in the maxilla, while Class A was the most prevalent in the mandible. The most prevalent impaction in terms of angulation in relation to the second molar was vertical in the maxilla and vertical and mesioangular in the mandible. Conclusion: Based on results, the prevalence of impacted wisdom teeth in patients was 17.69%. This may not be a striking amount, but it is still of significance since the possible complications of impacted teeth are costly and problematic

Clinical Findings of Patients with Dental Emergencies

Background and purpose: Some patients need emergency dental care and people with low economic status are more likely to go to hospital emergency departments. There is lack of enough knowledge about dental emergencies and no accurate data is available on this issue. This study aimed at investigating clinical and epidemiological characteristics of patients in dental emergency departments. Materials and methods: A cross-sectional descriptive study was performed in patients with dental complaints attending emergency departments affiliated with Mazandaran University of Medical Sciences, 2017-2018. Data were analyzed in SPSS V 16. Results: A total of 1660 patients (mean age: 36.80±15.50 years) were studied. The most common reasons for attending the emergency department were dental trauma (n=886) and dental infections (n=353). The highest referral times were evenings and nights (n=1130). Compared with women, men were found with more dental trauma and soft tissue injuries (P= 0.0001). Conclusion: Special training programs should be provided to the staff in hospital emergency departments so that they can obtain sufficient information and skills about triage, diagnosis, treatment, and follow-up of dental emergencies

A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.Comment: 33 page- 177 reference

Relationship between type 2 diabetic retinopathy and periodontal disease in Iranian Adults

Background: Periodontal disease in diabetic patients can compromise a patient′s ability to maintain a proper metabolic control and may be associated with diabetic complication. Aims: This study was designed to evaluate the frequency of periodontal disease in patients with type 2 diabetes mellitus (DM) and how this was related with the presence of diabetic retinopathy (DR). Materials and Methods: A comparison was made of periodontal parameters (plaque index (PI), community periodontal index of treatment needs (CPITN), periodontal disease severity measured in quartiles of probing depth (PD), and clinical attachment loss (CAL)) in a group of diabetic patients with retinopathy (n = 84) versus a group of diabetic patients without retinopathy (n = 129). In addition, 73 age- and sex-matched individuals were selected to serve as the control group. Analysis was performed to evaluate the relationships between periodontal disease and DR. Results: In terms of PI, no statistically significant differences were observed, so, oral hygiene was similar in both groups. Diabetic patients with retinopathy had greater CPITN (P < 0.001) and more severe periodontal disease (P < 0.001) than no retinopathy. Also, our results indicated a relationship between type 2 DM and periodontal disease. Conclusions: The patients with diabetes retinopathy appear to show increased periodontal disease susceptibility

Inspection Methods for 3D Concrete Printing

3D Concrete Printing (3DCP) is being used for off-site manufacture of many elements found in the built environment, ranging from furniture to bridges. The advantage of these methods is the value added through greater geometrical freedom because a mould is not needed to create the form. In recent years, research has focused on material properties both in the wet and hardened state, while less attention has been paid to verifying printed forms through geometry measurement. Checking conformity is a critical aspect of manufacturing quality control, particularly when assembling many components, or when integrating/interfacing parts into/with existing construction. This paper takes a case study approach to explore applications of digital measurement systems prior to, during, after manufacture using 3DCP and after the assembly of a set of 3DCP parts and discusses the future prospects for such technology as part of geometry quality control for the procurement of 3DCP elements for the built environment