Automated vision tracking of project related entities

Tracking of project related entities such as construction equipment, materials, and personnel is used to calculate productivity, detect travel path conflicts, enhance the safety on the site, and monitor the project. Radio frequency tracking technologies (Wi-Fi, RFID, UWB) and GPS are commonly used for this purpose. However, on large-scale sites, deploying, maintaining and removing such systems can be costly and time-consuming. In addition, privacy issues with personnel tracking often limits the usability of these technologies on construction sites. This paper presents a vision based tracking framework that holds promise to address these limitations. The framework uses videos from a set of two or more static cameras placed on construction sites. In each camera view, the framework identifies and tracks construction entities providing 2D image coordinates across frames. Combining the 2D coordinates based on the installed camera system (the distance between the cameras and the view angles of them), 3D coordinates are calculated at each frame. The results of each step are presented to illustrate the feasibility of the framework.

Pothole detection in asphalt pavement images

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.

Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure

Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds.© 2012 Elsevier Ltd. All rights reserved.

Management and Analysis of Unstructured Construction Data Types

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments

The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method.

A Videogrammetric As-Built Data Collection Method for Digital Fabrication of Sheet Metal Roof Panels

A roofing contractor typically needs to acquire as-built dimensions of a roof structure several times over the course of its build to be able to digitally fabricate sheet metal roof panels. Obtaining these measurements using the exiting roof surveying methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards. This paper presents a video-based surveying technology as an alternative method which is simple to use, automated, less expensive, and safe. When using this method, the contractor collects video streams with a calibrated stereo camera set. Unique visual characteristics of scenes from a roof structure are then used in the processing step to automatically extract as-built dimensions of roof planes. These dimensions are finally represented in a XML format to be loaded into sheet metal folding and cutting machines. The proposed method has been tested for a roofing project and the preliminary results indicate its capabilities.

Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.

Study of GaN epilayers growth on freestanding Si cantilevers

Five-micron thick freestanding Si cantilevers were fabricated on bulk Si (1 1 1) substrates with surface/bulk micromachining (SBM) process. Then 1-mu m thick GaN layers were deposited on the Si cantilevers by metal-organic chemical vapor deposition (MOCVD). Epilayers on cantilever areas were obtained crack-free, and the photoluminescence (PL) spectra verified the stress reduction and better material quality in these suspended parts of GaN. Back sides of the cantilevers were also covered with GaN layers, which prevented the composite beams from bending dramatically. This paper had proved the feasibility of integrating high-quality GaN epilayers with Si micromechanical structures to realize GaN-based micro electro-mechanical system (MEMS). (C) 2009 Elsevier Ltd. All rights reserved.

A Novel Geometric Algorithm for Blind Image Restoration Based on High-Dimensional Space

A novel geometric algorithm for blind image restoration is proposed in this paper, based on High-Dimensional Space Geometrical Informatics (HDSGI) theory. In this algorithm every image is considered as a point, and the location relationship of the points in high-dimensional space, i.e. the intrinsic relationship of images is analyzed. Then geometric technique of "blurring-blurring-deblurring" is adopted to get the deblurring images. Comparing with other existing algorithms like Wiener filter, super resolution image restoration etc., the experimental results show that the proposed algorithm could not only obtain better details of images but also reduces the computational complexity with less computing time. The novel algorithm probably shows a new direction for blind image restoration with promising perspective of applications.

Silicon: the promising material for phontonic integrated circuits platform

The development of optical network demands integrated arid multiple functionality modules to lowing cost and acquire highly reliability. Among the various contender materials to be photonic integrated circuits platform, silicon exhibits dominant characteristics and is the most promising platform materials. The paper compares the characteristics of some candidate materials with silicon and reviews recent progress in silicon based photonic integration technology. Tile challenges to silicon for optical integration for optical networking application arc also indicated.

Study on the forecast effects of PI method to the north and southwest China

In this paper, the codes of Pattern Informatics (PI) method put forward by Rundle et al. have been worked out according to their algorithm published, and the retrospective forecast of PI method to North China (28.0 degrees-42.0 degrees N, 108.0 degrees-125.0 degrees E) and to Southwest China (22.0 degrees-28.3 degrees N, 98.0 degrees-106.0 degrees E) has been tested. The results show that the hit rates in different regions show a great difference. In Southwest China, 32 earthquakes with M(L)5.0 or larger have occurred during the predicted time period 2000-2007, and 26 out of the 32 earthquakes occurred in or near the hot spots. In North China, the total number of M(L)5.0 or larger was 12 during the predicted time period 2000-2007, and only 3 out of the 12 earthquakes occurred in or near the hot spots. From our results, we hold that if the PI method could be applied to all kinds of regions, the parameters associated with time points and time windows should be chosen carefully to obtain the higher hit rate. We also found that the aftershocks in a strong earthquake sequence affect the PI results obviously. Copyright (c) 2009 John Wiley & Sons, Ltd.

Study on the forecast effects of PI method to the north and southwest China

In this paper, the codes of Pattern Informatics (PI) method put forward by Rundle et al. have been worked out according to their algorithm published, and the retrospective forecast of PI method to North China (28.0 degrees-42.0 degrees N, 108.0 degrees-125.0 degrees E) and to Southwest China (22.0 degrees-28.3 degrees N, 98.0 degrees-106.0 degrees E) has been tested. The results show that the hit rates in different regions show a great difference. In Southwest China, 32 earthquakes with M(L)5.0 or larger have occurred during the predicted time period 2000-2007, and 26 out of the 32 earthquakes occurred in or near the hot spots. In North China, the total number of M(L)5.0 or larger was 12 during the predicted time period 2000-2007, and only 3 out of the 12 earthquakes occurred in or near the hot spots. From our results, we hold that if the PI method could be applied to all kinds of regions, the parameters associated with time points and time windows should be chosen carefully to obtain the higher hit rate. We also found that the aftershocks in a strong earthquake sequence affect the PI results obviously. Copyright (c) 2009 John Wiley & Sons, Ltd.