Cam2 Measure 10 Training Manual Pdf

[Janae Chebret]

TheLTI 20/20 TruCAM II is designed for international use, and is not available in the USA. Boasting a large 9.4 cm trans-reflective LCD screen and other cutting edge features, the TruCAM II is the ultimate traffic and speed enforcement laser with video available on the market. The improvements in technology allow Laser Tech (LTI) to provide exciting new enhancements to the TruCAM II laser.

Speeding, tailgating, distracted driving and other traffic violations have met their match. The TruCAM II provides a high-resolution image which shows the license plate number as well as a full-length video of the violation like its predecessor, the original TruCAM. The upgrades for this traffic and speed enforcement laser allow officers the utmost in versatility.

More than just a speed enforcement laser, you can use the TruCAM II to gather the proof needed for other traffic violations including following too closely, aggressive driving, misuse of HOV lanes, distracted driving, obstructing traffic and seat belt violations.

Our patented DBC (Distance Between Cars) technology measures the time and distance between vehicles giving you the capability to prove following too closely / tailgating violations. Identifying motorcycles or vehicles without front license plates is not a problem when using the rear-plate mode on the TruCAM II.

Built-in detection algorithms combat laser jammers and our tamper-proof secure data encryption gives you the confidence and reliability in the courtroom, a reputation that LTI has built and maintained all over the world. There are currently over 6,000 photo/video lasers manufactured by LTI being used in more than 90 countries today.

LaserSoft ShareView software is included with your TruCAM II. This allows for an officer to automatically transfer files from the speed gun to a PC. This is a live feed that can also be used for a group training exercise.

Pavemetrics Laser Crack Measurement System (LCMS-2) is the ultimate single-pass 3D sensor for pavement inspection. The LCMS-2 is able to automatically geo-tag, measure, detect and quantify all key functional parameters of pavement in a single pass, including (but not limited to): cracking, rutting, texture, potholes, bleeding, shoving, raveling and roughness.

The LCMS-2 delivers standards-compliant and industry-proven results on more surfaces than any other sensor in the market; from hotmix asphalt to chipseal, porous pavement, and both standard and grooved (tined) concrete.

The LCMS-2 is the highly-anticipated successor to the incredibly popular LCMS-1. More than 100 LCMS-1 units have been delivered around the world; making it the most widely adopted and trusted sensor of its kind.

Author: Aziz Salifu and Nichole Andre (Saskatchewan Ministry of Highways and Transportation)

Abstract: The Saskatchewan Ministry of Highways and Infrastructure (SMHI) adopted Laser Crack Measuring System (LCMS) technology for collecting road condition data in 2016. LCMS data has replaced a visual assessment method for identifying cracking and other surface distresses. This paper discusses the methodology used to determine type, severity, extent and aggregation of LCMS distress data. To better analyze the data, SMHI developed the Surface Condition Indicator (SCI) to support asset management decision making for setting performance measures, optimize budgets, and identify pavement preservation candidates.

Using Full Lane 3D Road Texture Data for the Automated Detection of Sealed Cracks, Bleeding and Raveling

Authors: John Laurent, Jean-Franois Hbert and Mario Talbot (Pavemetrics)

Abstract: 3D transverse profiling techniques such as the LCMS (Laser Crack Measurement System) have proven reliable at detecting open cracks these systems have not been widely used to evaluate road texture. This article will present test results from the New Zealand Highway Authority (NZHA) that demonstrate that 3D transverse profiling lasers (LCMS) can be used to measure macro-texture as accurately as a single point texture lasers. Furthermore, because transverse profiling lasers measure texture on the entire road surface we will demonstrate that they can also be used to detect important surface features (sealed cracks, bleeding and raveling) that are missed by single point lasers.

3D Technology for Managing Pavements

Authors: Richard Wix and Roland Leschinski (ARRB Group)

Abstract: Advances in instrumentation have led to the development of new technologies that provide a number of options for collecting pavement condition data. Manual methods have been successfully replicated, automated and then further improved. For instance, 3D laser sensors were first introduced as a means of measuring the transverse profile of the pavement in much greater detail than a straight edge or even a multi-point laser profiler. However, with further advancements this technology is now being used to identify cracks and other defects in the pavement surface. This paper looks at how 3D technology can be used to measure pavement cracking as well as other pavement condition parameters that are of interest to state and local government agencies.

Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using 3D Laser Technology

Authors: Feng Li and Yichang James Tsai Georgia Institute of Technology

Feasibility Study of Measuring Concrete Joint Faulting Using 3D continuous Pavement Profile Data

Authors: Yichang James Tsai, Yiching Wu and Chengbo Ai (Georgia Institute of Technology)

Abstract: Faulting is one of the important performance measurements for jointed concrete pavements, as it has a direct impact on ride quality. Faulting has traditionally been measured manually using hand-held devices, such as the Georgia fault meter. However, manually measuring faulting on the roadways is labor intensive, time-consuming, and hazardous to workers and drivers. There is a need to develop alternative methods for effectively and safely collecting faulting data on each joint at highway speed. This paper proposes a new method to collect faulting data at highway speed using the 3D continuous pavement profile data acquired with emerging 3D laser technology and assesses its feasibility in field tests. While 3D continuous pavement profile data is initially used to detect asphalt pavement cracking and rutting, this paper further explores its use on concrete faulting measurement. Controlled field tests were conducted using artifacts with known elevation differences, and results show the proposed method can achieve desirable accuracy and repeatability with an absolute difference of less than 0.6 mm (0.024 inches) and a standard deviation of less than 0.4 mm (0.016 inches). Field tests were conducted on 15 joints on Interstate 16 (I-16) in Georgia, and preliminary results show that operating the proposed system at highway speeds (e.g. 100 km/hr) is feasible and has reasonable repeatability. Two tests have demonstrated the proposed method is very promising for providing an alternative solution to collect joint faulting data at highway speed. Recommendations for future research are also discussed.

Development of an Asphalt Pavement Raveling Detection Algorithm Using Emerging 3D Laser Technology and Macrotexture Analysis

Authors: Yichang James Tsai and Zhaohua Wang (Georgia Institute of Technology)

Abstract: Raveling is one of the most common asphalt pavement distresses that occur on U.S. highway pavements. Raveling will increase pavement roughness, which results in poor ride quality and road and tire noise. Besides safety concerns, such as loose stones that may break windshield glass and can cause hydroplaning, raveling will also shorten pavement longevity. Thus, a raveling condition survey is required for highway agencies to determine the severity levels, the extents, and the locations of raveling so the preservation or rehabilitation treatments can be appropriately applied. However, the traditional raveling survey method, including determination of the raveling severity level (e.g., Low, Moderate, or High; or Severity Level 1, 2, or 3), extent, and location is a visual inspection that is time consuming, subjective, and hazardous to highway workers. Thus, there is an urgent need for developing an automatic survey method. Although some algorithms have been developed to detect and classify raveling, they are still at the very early research stage and the outcomes were often not acceptable. In addition, they have not been thoroughly validated using large-scale, real-world data. Therefore, it has been difficult for transportation agencies to implement any of such algorithms. To address the problems in existing raveling detection and classification methods, the objective of this study is to develop successful and effective raveling detection, classification, and measurement algorithms using three-dimensional (3D) pavement data and macro-texture analysis, and to comprehensively validate these methods using large-scale, real-world data.

Automated Detection of Sealed Cracks Using 2D and 3D Road Surface Data

Authors: John Laurent, Jean-Franois Hbert and Mario Talbot (Pavemetrics)

Abstract: Reliable cracking data has proven difficult and expensive to obtain using cameras and video systems because of the lack of good automated 2D image processing crack detection algorithms. To solve this problem, 3D technology such as the LCMS (Laser Crack Measurement System) has been used to obtain automated reliable and repeatable cracking data. The LCMS system has been widely used for automated crack detection on a variety of road surfaces (DGA, porous, chipseal, concrete) in over 35 different countries. While 3D techniques have proven reliable at detecting open cracks these systems have not been used for detecting sealed cracks. These sensors however also often produce intensity (2D) images that are used to detect lane markings. Using this intensity (2D) data for the automated detection of sealed cracks has also proven unreliable because sealed cracks can sometimes be darker or brighter than the surrounding pavement in the images and tire marks and other features can also cause false detections. This article will demonstrate that the accuracy of sealed crack detection can be improved by using both 2D intensity data and 3D texture information evaluated from the 3D data. To do this 3D texture evaluation algorithms are described and implemented in order to generate a complete texture map of the road surface. The intensity images are also processed in order to extract dark and light areas of the appropriate geometry (size and shape of sealed cracks). The combination of the results from both sets of processed data is then used to detect and validate the presence of sealed cracks.

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