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.

scholarly journals Lane Detection on Roads using Computer Vision

2019 ◽  
Vol 9 (1) ◽  
pp. 1200-1205
Keyword(s):  
Image Features ◽  
Lane Detection ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Detection Techniques ◽  
The Road ◽  
Detection Systems ◽  
Feature Based ◽  
On The Road ◽  
Complex Goal

In recent times many technological advancements are coming in the domain of road safety as accidents has been increasing at an alarming rate and one of the crucial reason for such accidents is lack of driver’s attention. Technical advancements should be there to reduce the frequency of the accidents and stay safe. One of the way to achieve the same is through Lane Detection Systems which work with the intention to recognize the lane borders on road and further prompts the driver if he switches and moves to erroneous lane markings. Lane detecting system is an essential component of many technologically intelligent transport system. Although it’s a complex goal to achieve because of vacillating road conditions that a person encounters specially while driving at night or even in daylight. Lane boundaries is detected using a camera that captures the view of the road, mounted on the front of the vehicle. The approach used in this paper changes the image taken from the video into a set of sub-images and generates image-features for each of them which are further used to detect the lanes present on the roads. There are proposed numerous ways to detect the lane markings on the road. Feature-based or model-based are the two categories of the lane detection techniques. Down-level characteristics for example lane-mark edges are used by the feature-based functions.

A Study on Glaucoma Disease Detection with Image Processing Methods

2020 ◽  
Vol 37 ◽  
pp. 25-35
Author(s):  
Shashilata Rawat ◽  
Uma Shankar Kurmi
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Imaging Techniques ◽  
Advanced Imaging ◽  
Detection Techniques ◽  
Glaucoma Detection ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
The Brain ◽  
Aided Diagnosis

The glaucoma is a developing slow eye that effects optic nerve damage in its most common form. Once the optic nerve has been impaired, visual data is not passed to the brain and permanently visual impairment is caused. Glaucoma computer-aided diagnosis (CAD) is a rising area in which medical imaging is analyzed. The CAD is a more precise approach for glaucoma detection, inspired by recent advanced imaging techniques and high-velocity computers. Laser ophthalmoscope scanning, tomography with optical coherence, and retina tomography of Heidelberg have widely used imaging techniques for detecting glaucoma. In this paper, we provide a study of glaucoma disease with its types and detection techniques. Moreover, this paper tells about image processing techniques to detect glaucoma. Variational mode decomposition has also discussed here.

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 A Research: Hyperspectral Image Processing Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 577-581
Keyword(s):  
Image Processing ◽  
Dimension Reduction ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Target Identification ◽  
Target Classification ◽  
Detection Techniques ◽  
Hyperspectral Image Processing ◽  
Complicated Process ◽  
Processing Techniques

Hyperspectral image contains more information which are gathered from numerous narrow wavebands from one or more regions, and large amount of data are huddled. An basic problems in hyperspectral image processing are dimension reduction, target detection, target identification, and target classification. In this document, we reviewed the latest activities of target classification, most frequently used techniques for dimension reduction, target detection. Hyperspectral image processing is a complicated process which rely on mixed agents. Here we also recognized and reviewed problems faced by some methods and to overcome the problems, current techniques are discussed and highlighted good methods. To improving correctness, genuine classification techniques and Detection Techniques analysis are recommended

Hypertensive Retinopathy Classification Using Improved Clustering Algorithm and the Improved Convolution Neural Network

2022 ◽  
pp. 119-131
Author(s):  
Bhimavarapu Usharani
Keyword(s):  
Image Processing ◽  
Clustering Algorithm ◽  
Activation Function ◽  
Current Approach ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Image Processing Techniques ◽  
Detection Systems ◽  
Processing Techniques ◽  
Fuzzy Image

Hypertensive retinopathy is a disorder that causes hypertension which includes abnormalities in the retina that triggers vision problems. An effective automatic diagnosis and grading of the hypertensive retinopathy would be very useful in the health system. This chapter presents an improved activation function on the CNN by recognizing the lesions present in the retina and afterward surveying the influenced retina as indicated by the hypertensive retinopathy various sorts. The current approach identifies the symptoms associated of retinopathy for hypertension. This chapter presents an up-to-date review on hypertensive retinopathy detection systems that implement a variety of image processing techniques, including fuzzy image processing, along various improved activation function techniques used for feature extraction and classification. The chapter also highlights the available public databases, containing eye fundus images, which can be currently used in the hypertensive retinopathy research.

Available Lane Detection Based on Radon Transform

2014 ◽  
Vol 1046 ◽  
pp. 415-424
Author(s):  
Xi Xi Deng ◽  
Xiao Nian Wang ◽  
Jin Zhu
Keyword(s):  
Radon Transform ◽  
Autonomous Vehicle ◽  
Perspective Transformation ◽  
Lane Detection ◽  
Detection Problem ◽  
The Road ◽  
Texture Information

To solve lane detection problem in the system of autonomous vehicle, this paper proposes a method of texture segment based on perspective transformation. In this paper, firstly road images were captured through cameras installed on the vehicle, then make a perspective transform to road plane, so that the road and the non-road texture information effectively stand out. After calculation of the texture trend in the transformed image, radon transform can effectively distinguish between the road and the non-road area, and achieve the purpose of the texture regional segment. Experiments prove that this method can be used on the lane detection, which eliminate barriers and road borders effectively.

scholarly journals Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020 ◽  
Vol 10 (7) ◽  
pp. 2372
Author(s):  
Byambaa Dorj ◽  
Sabir Hossain ◽  
Deok-Jin Lee
Keyword(s):  
Kalman Filter ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Self ◽  
Full Curve ◽  
The Road ◽  
Detection Algorithms ◽  
Simulation Results

The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

scholarly journals Vision Based Displacement Detection for Stabilized UAV Control on Cloud Server

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hyeok-June Jeong ◽  
Jeong Dan Choi ◽  
Young-Guk Ha
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Functional Performance ◽  
Vertical Movement ◽  
Traffic Information ◽  
Detection Systems ◽  
Speeded Up Robust Features ◽  
Aerial Vehicle ◽  
External Forces ◽  
Processing Techniques

Nowadays, image processing solution is used in many fields such as traffic information systems and illegal intrusion detection systems. Now, to assist with the control of camera-equipped devices, appropriate image processing techniques are needed for moving rather than fixed observers. For achieving this goal, an algorithm should derive the desired results quickly and accurately; thus, this paper considers two characteristics: functional performance (reliability) and temporal performance (efficiency). Reliability means how well the desired results can be achieved, and efficiency means how quickly the result can be calculated. This paper suggests an optimized real-time image algorithm based on the integration of the optical flow and Speeded-Up Robust Features (SURF) algorithms. This algorithm determines horizontal or vertical movement of the camera and then extracts its displacement. The proposed algorithm can be used to stabilize an Unmanned Aerial Vehicle (UAV) in situations where it is drifting due to inertia and external forces, like wind, in parallel. The proposed algorithm is efficient in achieving drift stabilization by movement detection; however, it is not appropriate for image processing in small UAVs. To solve this problem, this study proposes an image processing method that uses a high-performance computer.

scholarly journals Automated Pavement Distress Detection Using Image Processing Techniques

2021 ◽  
Vol 11 (5) ◽  
pp. 7702-7708
Author(s):  
I. H. Abbas ◽  
M. Q. Ismael
Keyword(s):  
Image Processing ◽  
Surface Damage ◽  
Condition Index ◽  
Asphalt Pavements ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Automated Method ◽  
Pavement Condition Index ◽  
Processing Techniques

Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels with a medium validity of 76%. There are two kinds of methods, manual and automated, for distress evaluation that are used to assess pavement condition. A committee of three expert engineers in the maintenance department of the Mayoralty of Baghdad did the manual assessment of a highway in Baghdad city by using a Pavement Condition Index (PCI). The automated method was assessed by processing the videos of the road. By comparing the automated with the manual method, the accuracy percentage for this case study was 88.44%. The suggested method proved to be an encouraging solution for identifying cracks and potholes in asphalt pavements and sorting their severity. This technique can replace manual road damage assessment.

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