3,345 research outputs found
Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery
One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%
Unmanned Aircraft System Assessments of Landslide Safety for Transportation Corridors
An assessment of unmanned aircraft systems (UAS) concluded that current, off-the-shelf UAS aircraft and cameras can be effective for
creating the digital surface models used to evaluate rock-slope stability and landslide risk along transportation corridors. The imagery
collected with UAS can be processed using a photogrammetry technique called Structure-from-Motion (SfM) which generates a point
cloud and surface model, similar to terrestrial laser scanning (TLS). We treated the TLS data as our control, or “truth,” because it is a
mature and well-proven technology. The comparisons of the TLS surfaces and the SFM surfaces were impressive – if not comparable is
many cases. Thus, the SfM surface models would be suitable for deriving slope morphology to generate rockfall activity indices (RAI)
for landslide assessment provided the slopes. This research also revealed that UAS are a safer alternative to the deployment and
operation of TLS operating on a road shoulder because UAS can be launched and recovered from a remote location and capable of
imaging without flying directly over the road. However both the UAS and TLS approaches still require traditional survey control and
photo targets to accurately geo-reference their respective DSM.List of Figures ...................................................................................................... vi
List of Abbreviations ......................................................................................... vii
Acknowledgments ................................................................................................ x
Executive Summary ............................................................................................. xi
CHAPTER 1 INTRODUCTION .......................................................................... 1
CHAPTER 2 LITERATURE REVIEW ................................................................ 4
2.1 Landslide Hazards .................................................................................... 4
2.2 Unmanned Aircraft Systems Remote Sensing.......................................... 6
2.3 Structure From Motion (SfM) .................................................................. 7
2.4 Lidar terrain mapping ............................................................................... 8
CHAPTER 3 STUDY SITE/DATA .................................................................. 11
CHAPTER 4 METHODS ................................................................................ 13
4.1 Data Collection ............................................................................................. 13
4.1.1 Survey Control ..................................................................................... 14
4.1.2 TLS Surveys ........................................................................................ 16
4.1.3 UAS Imagery ....................................................................................... 17
4.1.4 Terrestrial Imagery Acquisition ........................................................... 19
4.2 Data Processing ............................................................................................ 20
4.2.1 Survey Control ..................................................................................... 20
4.2.2 TLS Processing .................................................................................... 20
4.2.3 SfM Processing .................................................................................... 21
4.2.4 Surface Generation .............................................................................. 22
4.3 Quality Evaluation ........................................................................................ 23
4.3.1 Completeness ....................................................................................... 23
4.3.2 Data Density/Resolution ...................................................................... 23
4.3.3 Accuracy Assessment .......................................................................... 23
4.3.2 Surface Morphology Analysis ............................................................. 24
4.2.6 Data Visualization ............................................................................... 25
CHAPTER 5 RESULTS ................................................................................. 27
v
5.1 UTIC DSM evaluation.................................................................................. 27
5.1.1 Completeness evaluation ..................................................................... 28
5.1.2 Data Density Evaluation ...................................................................... 29
5.1.3 Accuracy Evaluation............................................................................ 30
5.2 Geomorphological Evaluation ...................................................................... 32
CHAPTER 6 DISCUSSION ............................................................................ 35
6.1 Evaluation of UAS efficiencies .................................................................... 35
6.2 DSM quality and completeness .................................................................... 37
6.3 Safety and operational considerations .......................................................... 37
CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS ................................ 40
7.1 Technology Transfer..................................................................................... 41
7.1.1 Publications ......................................................................................... 41
7.1.2 Presentations ........................................................................................ 42
7.1.3 Multi-media outreach .......................................................................... 43
6.4 Integration of UAS and TLS data ................................................................. 44
REFERENCES .............................................................................................. 4
Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters
Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation
Measurement and Evaluation of Roadway Geometry for Safety Analyses and Pavement Material Volume Estimation for Resurfacing and Rehabilitation Using Mobile LiDAR and Imagery-based Point Clouds
Roadway safety is a multifaceted issue affected by several variables including geometric design features of the roadway, weather conditions, sight distance issues, user behavior, and pavement surface condition. In recent years, transportation agencies have demonstrated a growing interest in utilizing Light Detecting and Ranging (LiDAR) and other remote sensing technologies to enhance data collection productivity, safety, and facilitate the development of strategies to maintain and improve existing roadway infrastructure. Studies have shown that three-dimensional (3D) point clouds acquired using mobile LiDAR systems are highly accurate, dense, and have numerous applications in transportation. Point cloud data applications include extraction of roadway geometry features, asset management, as-built documentation, and maintenance operations. Another source of highly accurate 3D data in the form of point clouds is close-range aerial photogrammetry using unmanned aerial vehicle (UAV) systems. One of the main advantages of these systems over conventional surveying methods is the ability to obtain accurate continuous data in a timely manner. Traditional surveying techniques allow for the collection of road surface data only at specified intervals. Point clouds from LiDAR and imagery-based data can be imported into modeling and design software to create a virtual representation of constructed roadways using 3D models.
From a roadway safety assessment standpoint, mobile LiDAR scanning (MLS) systems and UAV close-range photogrammetry (UAV-CRP) can be used as effective methods to produce accurate digital representations of existing roadways for various safety evaluations. This research used LiDAR data collected by five vendors and UAV imagery data collected by the research team to achieve the following objectives: a) evaluate the accuracy of point clouds from MLS and UAV imagery data for collection roadway cross slopes for system-wide cross slope verification; b) evaluate the accuracy of as-built geometry features extracted from MLS and UAV imagery-based point clouds for estimating design speeds on horizontal and vertical curves of existing roadways; c) Determine whether MLS and UAV imagery-based point clouds can be used to produce accurate road surface models for material volume estimation purposes. Ground truth data collected using manual field survey measurements were used to validate the results of this research.
Cross slope measurements were extracted from ten randomly selected stations along a 4-lane roadway. This resulted in a total of 42 cross slope measurements per data set including measurements from left turn lanes. The roadway is an urban parkway classified as an urban principal arterial located in Anderson, South Carolina. A comparison of measurements from point clouds and measurements from field survey data using t-test statical analysis showed that deviations between field survey data and MLS and UAV imagery-based point clouds were within the acceptable range of ±0.2% specified by SHRP2 and the South Carolina Department of Transportation (SCDOT).
A surface-to-surface method was used to compute and compare material volumes between terrain models from MLS and UAV imagery-based point clouds and a terrain model from field survey data. The field survey data consisted of 424 points collected manually at sixty-nine 100-ft stations over the 1.3-mile study area. The average difference in height for all MLS data was less than 1 inch except for one of the vendors which appeared to be due to a systematic error. The average height difference for the UAV imagery-based data was approximately 1.02 inches. The relatively small errors indicated that these data sets can be used to obtain reliable material volume estimates.
Lastly, MLS and UAV imagery-based point clouds were used to obtain horizontal curve radii and superelevation data to estimate design speeds on horizontal curves. Results from paired t-test statistical analyses using a 95% confidence level showed that geometry data extracted from point clouds can be used to obtain realistic estimates of design speeds on horizontal curves. Similarly, road grade and sight distance were obtained from point clouds for design speed estimation on crest and sag vertical curves. A similar approach using a paired t-test statistical analysis at a 95% confidence level showed that point clouds can be used to obtain reliable design speed information on crest and sag vertical curves. The proposed approach offers advantages over extracting information from design drawings which may provide an inaccurate representation of the as-built roadway
Land use, urban, environmental, and cartographic applications, chapter 2, part D
Microwave data and its use in effective state, regional, and national land use planning are dealt with. Special attention was given to monitoring land use change, especially dynamic components, and the interaction between land use and dynamic features of the environment. Disaster and environmental monitoring are also discussed
Smart License Plate Recognition Using Optical Character Recognition Based on the Multicopter
In recent years Unmanned Aerial Vehicle (UAV) is major focused of active research, since they can extend our capabilities in a variety of areas, especially for application like research detection, tracking and recognition. For our project goals is vehicle tracking and plate recognition. In addition, we have to combine some intelligence algorithms. In this project to define the number and type of vehicles, using our nation's roadways is becoming more and more important. This project used for Multicopter. The multicopter to flying around of the roadway. Because it is to collect roadway’s data. That means, to send a picture of a vehicle violating the law. Then our algorithm is recognizing to the number plate. In addition, this algorithm saving the vehicle number plate. We are great database in this algorithm. In this paper, template matching algorithm for character recognition is used. The developed system first detects the vehicle and capture the image. Then vehicle number plate region is extracted using the image segmentation in an image. Character recognition algorithm working on the OCR algorithm. We are detection accuracy to increase by using some algorithms. We combined these different algorithms using a modified version of PCA and OCR recognizer, we designed the proposed an architecture using OpenCV and we used to implement the design in the Multicopter
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