Research on Lane Marking Lines Detection

2013 ◽  
Vol 274 ◽  
pp. 634-637
Author(s):  
Hui Tan ◽  
Jian Feng Wang ◽  
Kun Zhang ◽  
Sheng Min Cui
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Traffic Accidents ◽  
Hot Spot ◽  
Intelligent Vehicles ◽  
Vehicle Navigation ◽  
Research Directions ◽  
Vision Navigation ◽  
Lane Marking ◽  
Simulation Results

Nowadays traffic accidents occur more and more frequently, as a result, intelligent vehicles develop more and more quickly. In many research directions of intelligent vehicles, vision navigation becomes the hot spot. An algorithm of the present lane left-right marking lines detection was proposed in this paper. The algorithm combines edge detection and Hough transform, firstly detects the initial lane marking lines and then tracks the final target lines. Simulation results indicated that the algorithm could recognize the present lane marking lines to make vehicle navigation precise and fast.

A Review of Multiple Edge Detection in Road Lane Detection Using Improved Hough Transform

2015 ◽  
Vol 1125 ◽  
pp. 541-545 ◽  
Author(s):  
Muhamad Lazim Talib ◽  
Suzaimah Ramli
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Autonomous Navigation ◽  
Traffic Accidents ◽  
Detection System ◽  
Lane Detection ◽  
Safe Driving ◽  
Driving Experience ◽  
Detection Techniques ◽  
Selection Of

Lane detection system for the driver of the car is an important issue for the inquiry as a platform for safe driving experience. Implementation of this system is trying to investigate the possibility of traffic accidents, monitor the efficiency of the movement and position of the car contributes to the development of autonomous navigation technology. The purpose of this study is to get the best selection of banks in a better Hough transform technique to detect lane roads using edge detection techniques. For this study, Canny, Sobel and Prewitt edge detection is used as a trial. Selection of the best edge detection was using neural network techniques. Improved Hough Transform is used to extract features of a structured road. Point area near the straight line model adopted to accelerate the speed of calculation data and find the appropriate line. Prior knowledge is used in the process of finding a path to efficiently reduce the Hough space efficiently, thereby increasing the resistance by increasing the processing speed. Experiments provide good results in detecting straight and smooth fair curvature lane on highway even the hallways are painted shadows. Data from the lane highways have been taken in video format. Experiments have been done using an edge detection technique of choice in each scenario, and found that the best method of producing high accuracy of detection is to use intelligent edge detector. In this way, other people will be the best in certain cases scenarios lane highway.

scholarly journals Feature Extraction of Gear Fault Signal Based on Sobel Operator and WHT

2013 ◽  
Vol 20 (3) ◽  
pp. 551-559 ◽  
Author(s):  
Jian-Hua Cai ◽  
Wei-Wen Hu
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Cross Term ◽  
Time Performance ◽  
Sobel Operator ◽  
Gear Fault ◽  
Influence Of Noise

Taking Wigner-Ville distribution of gear fault signal as a picture,Sobeloperator was applied for edge detection of picture and then Hough transform was used to extract signal feature. Some simulated and measured signals have been processed to demonstrate the effectiveness of new method, which was compared with traditional Wigner-Hough transform and SPWD-Hough transform. The results show that the proposed method can suppress cross term which is produced from using Wigner-Ville distribution to analyze multi-component signal, especially under the condition of low signal to noise ratio. The improved Wigner-Hough transform can effectively suppress the influence of noise and has a good real-time performance because its algorithm is fast. The proposed method provides an effective method to determine the state of gear accurately.

Improved Hough Transform Applying in Mobile Robot Vision Navigation

2012 ◽  
Vol 232 ◽  
pp. 408-413
Author(s):  
Yin Ping Jiang ◽  
Xian Xian Zhang ◽  
Xiao Peng Fu
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Robot Vision ◽  
Geometric Figure ◽  
Vision Navigation ◽  
Line Extraction ◽  
Local Threshold ◽  
Global Threshold ◽  
Image Quality Reduction ◽  
Mobile Robot Vision

This paper mainly discusses that in mobile robot vision navigation system, by using the improved Hough transform, we can improve the accuracy of line extraction and therefore avoid the image quality reduction caused by noise points. Considering the limitations of the standard Hough transform, we come up with a method with which we will accumulates the H (ρ, θ) through distributing the increment value, set a global threshold to shun the pointless measurements, eliminate the false lines by comparing θ difference between tow arbitrary lines, find the peaks by using rectangle window, and set a local threshold to eliminate false peaks. In this way, we can gain a method superior to the standard Hough transform which works better in extracting lines in application. The experiments show that this method can not only extract line features of geometric figure effectively in brief background, but also eliminate the iterative lines efficiently.

ISAR Imaging of Multiple Targets Using Edge Detection and Hough Transform

2008 ◽  
Vol 22 (2-3) ◽  
pp. 365-373 ◽  
Author(s):  
S. H. Park ◽  
K. K. Park ◽  
J. H. Jung ◽  
H. T. Kim ◽  
K. T. Kim
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Multiple Targets ◽  
Isar Imaging

Vision-Based Route Recognition of AGV on ARM Platform

2013 ◽  
Vol 385-386 ◽  
pp. 1309-1312
Author(s):  
Xiao Hua Huang ◽  
Ke Wang ◽  
Ze Yi Huang ◽  
Chun Peng Chen
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Hough Transform ◽  
High Performance ◽  
Embedded Platform

The AGV based on visional route recognition need to deal with problems of random interference of environment and real-time of algorithm. In this paper, an embedded platform based on ARM Cortex-A8 hardware is established, on which an implementation of the optimized algorithm is tested. The first algorithm of route recognition is based on binary spilt and morphology operator, and the second one is based on edge detection operators and Hough transform. The result of the experiment indicates a high performance on anti-interference and real-time.

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