Study on Urban Expressway Lane Mark Detection in Special Conditions

2011 ◽  
Vol 305 ◽  
pp. 164-167
Author(s):  
Xin Sheng He ◽  
Shi Shi Wang ◽  
Zhi Yong Cai ◽  
Dong Yun Wang
Keyword(s):  
Warning System ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Gray Level ◽  
Test Results ◽  
The Road ◽  
Wide Range ◽  
Lane Line ◽  
Image Pixels ◽  
Gray Level Histogram

Detection algorithm of lane line under the special condition is based on focal and difficult point of lane line departure warning system of computer vision. This article firstly deals with the image compression and grayscale, establishes reasonable region of interest, and remove the non-road information in the image; Then we proceed the probabilistic and statistical computing for the image pixels, draw the gray level histogram. By analyzing the dynamic gray level histogram, we identify the lane line and grey value of the road and automatically calculate the reasonable threshold to binarizate then denoise the images. Last we label the images to reach the goal of identification of lane line and establishment the space between lane line and vehicles. The test results show that: the algorithm mentioned in this paper can not only detect the lane line accurately in real time, but also it enjoys a wide range of applicability to provide reference for improvement of lane line departure warning system.

scholarly journals Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

2018 ◽  
Vol 11 (6) ◽  
pp. 2455-2474 ◽  
Author(s):  
Christine A. Shields ◽  
Jonathan J. Rutz ◽  
Lai-Yung Leung ◽  
F. Martin Ralph ◽  
Michael Wehner ◽  
...  
Keyword(s):  
Experimental Design ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Tracking Method ◽  
Detection Algorithms ◽  
Atmospheric River ◽  
Wide Range ◽  
Tier 1 ◽  
Intercomparison Project ◽  
Two Phases

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

Research on Lane Departure Warning System

2014 ◽  
Vol 598 ◽  
pp. 731-735 ◽  
Author(s):  
Hui Bao Yang
Keyword(s):  
Hough Transform ◽  
Warning System ◽  
Region Of Interest ◽  
Lane Departure Warning ◽  
Linear Mode ◽  
Detection And Tracking ◽  
Lane Departure ◽  
Calculation Parameter ◽  
Lane Line ◽  
Lane Position

Lane departure warning system includes lane identification and lane departure determination. Lane identification is crucial for lane departure warning system. In this paper, A linear mode which can only bring out small distance error and angle error is used to detect lane boundaries. A region of interest (ROI) appropriate is set to reduce nonessential cost of computation. According to the characteristics of lane position, we improve the Hough transform, reduce the detection and tracking transform angle and raise the speed of calculation. Parameter angle form Hough transform is used to lane departure determination, this method does not calibrate the camera and can get the lane departure rate. Experiments show that the system is well to detect and track the lane line and give alarm correctly.

scholarly journals Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

2020 ◽  
Vol 10 (7) ◽  
pp. 2543 ◽  
Author(s):  
Jianjun Hu ◽  
Songsong Xiong ◽  
Yuqi Sun ◽  
Junlin Zha ◽  
Chunyun Fu
Keyword(s):  
Region Of Interest ◽  
Global Search ◽  
Lane Detection ◽  
Vehicle Speed ◽  
Feature Points ◽  
The Road ◽  
Detection Approach ◽  
Polar Coordinate ◽  
Previous Frame ◽  
Lane Line

A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.

scholarly journals Lane Detection and Lane Departure Warning System

2021 ◽  
Vol 10 (3) ◽  
pp. 2212-2218
Keyword(s):  
Autonomous Vehicles ◽  
Warning System ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Road Accidents ◽  
Lane Departure ◽  
Clustering And Classification ◽  
Canny Edge ◽  
Boundary Information

Lane detection is important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. Advanced driverassistance systems are developed to assist drivers in the driving process reducing road accidents. In this work, we present an end-to-end system for lane identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. The first step is camera calibration which is used to remove the effect of lens distortion. Then a canny edge detection algorithm finds the edges of the images. Then the region of interest (ROI) is selected. The ROI is actually based on the rectangular shape appearing at the bottom of the image. ROI removes the unwanted region in the image. The potential lane markers are then determined using the Hough transform to analyze lane boundaries. Once the lane pixels are found, these pixels are continuously scanned to obtain the best linear regression analysis.It is qualified to be applied on highways and urban roadways. It also has been successfully verified in sunny, and rainy conditions for both day and night.

scholarly journals Image Processing-based Flood Detection for Online Flood Early Warning System

2017 ◽  
Author(s):  
Yudha Maulana Akbar ◽  
Akhmad Musafa ◽  
Indra Riyanto
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Edge Points

This paper will discuss the design of an online flood early warning system. This system will use a single board computer Raspberry-PI as the main controller, and a webcam to capture image. This system is integrated to Twitter. In hardware section, Raspberry-PI has main tasks as an image processor and do an update request to Twitter. In software section, OpenCV will be used as Image Processing software. Some method which used in this system is: 1) Region of Interest: this method is to create a portion of an image that you want to filter or perform some other operation on. Brightness and contrast: these methos is used in order to get brighter and better image before next process. 3) Grayscale and threshold: this method is to create an object segmentation. Otsu-thresholding is used on this step. 4) Edge detection: edge detection algorithm to find edge points on a (relatively) horizontal water line and point of dam’s height. By using these methods, the system can read and monitor the water level in the dam. If the water level exceeds the specified threshold, this system will generate an early warning of impending floods by doing update time line (text and image) of water level conditions to Twitter. The public will get the information if they following early warning system’s Twitter. Simulation test results show the system can read water level with an accuracy nearing 96%.

scholarly journals Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project Goals and Experimental Design

2018 ◽  
Author(s):  
Christine A. Shields ◽  
Jonathan J. Rutz ◽  
Lai-Yung Leung ◽  
F. Martin Ralph ◽  
Michael Wehner ◽  
...  
Keyword(s):  
Experimental Design ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Tracking Method ◽  
Detection Algorithms ◽  
Atmospheric River ◽  
Wide Range ◽  
Tier 1 ◽  
Intercomparison Project ◽  
Two Phases

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof of concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.

scholarly journals A Real-Time Lane Detection Algorithm Based on Intelligent CCD Parameters Regulation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ping-shu Ge ◽  
Lie Guo ◽  
Guo-kai Xu ◽  
Rong-hui Zhang ◽  
Tao Zhang
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Real ◽  
Lane Departure ◽  
Lane Line ◽  
Onboard System ◽  
Complexity Of Algorithm

Lane departure warning system (LDWS) has been regarded as an efficient method to lessen the damages of road traffic accident resulting from driver fatigue or inattention. Lane detection is one of the key techniques for LDWS. To overcome the contradiction between complexity of algorithm and the real-time requirement for vehicle onboard system, this paper introduces a new lane detection method based on intelligent CCD parameters regulation. In order to improve the real-time capability of the system, a CCD parameters regulating method is proposed which enhances the contrast between lane line and road surfaces and reduces image noise, so it lays a good foundation for the following lane detection. Hough transform algorithm is improved by selection and classification of seed points. Finally the lane line is extracted through some restrictions. Experimental results verify the effectiveness of the proposed method, which improves not only real-time capability but also the accuracy of the system.

Typhoon Cloud Tracking by Kalman Filter

2011 ◽  
Vol 58-60 ◽  
pp. 2487-2492
Author(s):  
Ying Lv
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Region Of Interest ◽  
Experimental Results ◽  
Interactive Methods ◽  
Bézier Curve ◽  
Gray Level ◽  
Reference Region ◽  
Gray Level Histogram ◽  
Cloud Tracking

Typhoon cloud has its changeability, so it is difficult to track and predict compared with the rigid targets. Region of interest (ROI) and reference region were selected by using interactive methods. Bezier curve is used to smooth the gray level histogram of ROI and obtain Bezier histogram. The gray level value which is corresponding to the valley of the Bezier histogram is used to segment the ROI in order to get the tracking target. And target parameters could be predicted by using Kalman filter, then getting the moving track of the target. Experimental results show that the proposed algorithm has nice real-time ability and adaptability.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Evaluation and Avoidance of False Alarm by Controlling Rhine Water with Continuously Working Biotests

1994 ◽  
Vol 29 (3) ◽  
pp. 207-209 ◽  
Author(s):  
H. Puzicha
Keyword(s):  
Warning System ◽  
Point Sources ◽  
Trophic Levels ◽  
River Rhine ◽  
Wide Range ◽  
River Test ◽  
Continuous Working ◽  
Range Of Variation ◽  
Contaminant Load ◽  
Rhine Water

Effluents from point sources (industries, communities) and diffuse inputs introduce pollutants into the water of the river Rhine and cause a basic contaminant load. The aim is to establish a biological warning system to detect increased toxicity in addition to the already existing chemical-physical monitoring system. To cover a wide range of biocides, continuous working biotests at different trophic levels (bacteria, algae, mussels, water fleas, fishes) have been developed and proved. These are checked out for sensitivity against toxicants, reaction time, validity of data and practical handling under field conditions at the river. Test-specific appropriate methods are found to differentiate between the normal range of variation and true alarm signals.

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