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 Lane Detection Techniques using Image Processing

2021 ◽  
Vol 40 ◽  
pp. 03011
Author(s):  
Vighnesh Devane ◽  
Ganesh Sahane ◽  
Hritish Khairmode ◽  
Gaurav Datkhile
Keyword(s):  
Image Processing ◽  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Lane Detection ◽  
Time Curve ◽  
Detection Techniques ◽  
The Road ◽  
Detection Systems ◽  
Processing Techniques ◽  
Main Shortcoming

Lane detection is a developing technology that is implemented in vehicles to enable autonomous navigation. Most lane detection systems are designed for roads with proper structure relying on the existence of markings. The main shortcoming of these approaches is that they might give inaccurate results or not work at all in situations involving unclear markings or the absence of them. In this study one such approach for detecting lanes on an unmarked road is reviewed followed by an improved approach. Both the approaches are based on digital image processing techniques and purely work on vision or camera data. The main aim is to obtain a real time curve value to assist the driver/autonomous vehicle for taking required turns and not go off the road.

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.

scholarly journals Real-time Aggressive Driving Detection System based on In-vehicle Information using LoRa Communication

2020 ◽  
Vol 308 ◽  
pp. 06001
Author(s):  
Yongsu Jeon ◽  
Chanwoo Kim ◽  
Hyunwook Lee ◽  
Yunju Baek
Keyword(s):  
Real Time ◽  
Communication Networks ◽  
Traffic Accidents ◽  
Detection System ◽  
Aggressive Driving ◽  
Safe Driving ◽  
Driving Behaviors ◽  
Interior Data ◽  
Dangerous Driving ◽  
Vehicle Information

Safe driving has attracted a significant amount of attention in recent years owing to the increase in the complexity of the driving environment. There are many research studies focusing on detection of aggressive driving that may cause traffic accidents. In this paper, we propose a system for acquiring vehicles’ interior data and thereby detecting dangerous driving conditions. The system is designed to transmit the information acquired to a data server using Long Range (LoRa) communication networks. Through experimentation, we confirm that the proposed system can detect aggressive driving behaviors in real time and store them on the data server through LoRa communication. We evaluated techniques for acquiring in-vehicle information on 14 vehicles and confirmed that data can be extracted from most of the commonly available vehicles.

scholarly journals Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 999-1010
Author(s):  
Hayder G.A. Altameemi ◽  
Ahmed Abdul Azeez Ismael ◽  
Raddam Sami Mehsen
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Transformation Method ◽  
Fingerprint Recognition ◽  
Biometric Identification ◽  
Fingerprint Matching ◽  
Matching Algorithm ◽  
Detection Techniques ◽  
Matching Procedure ◽  
Prewitt Edge Detection

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

scholarly journals Analysis of a robust edge detection system in different color spaces using color and depth images

2019 ◽  
Vol 43 (4) ◽  
pp. 632-646
Author(s):  
S.M.H. Mousavi ◽  
V. Lyashenko ◽  
V.B.S. Prasath
Keyword(s):  
Edge Detection ◽  
Mean Squared Error ◽  
Detection System ◽  
Detection Methods ◽  
Morphological Operations ◽  
Color Spaces ◽  
Detection Techniques ◽  
Depth Data ◽  
Depth Images ◽  
Different Color

Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today’s systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are so promising and satisfactory in compare with other methods available in validation section of the paper.

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