Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features

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Open Journal of Applied Sciences

ISSN Print: 2165-3917
ISSN Online: 2165-3925
www.scirp.org/journal/ojapps
E-mail: ojapps@scirp.org

Google-based Impact Factor: 0.92
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"Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features"
written by Avishek Parajuli, Mehmet Celenk, H. Bryan Riley,
published by Open Journal of Applied Sciences, Vol.3 No.1B, 2013

has been cited by the following article(s):

Google Scholar
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[1]	A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle
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[2]	Exposure consistency for lane detection under varied light conditions
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[3]	Attention-based Row Selecting Networks for Lane Detection
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[4]	The Use of DCNN for Road Path Detection and Segmentation
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[5]	Real-Time Lane Detection by Using Biologically Inspired Attention Mechanism to Learn Contextual Information
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[6]	Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review
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[7]	Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study
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[8]	Color Masking Method for Variable Luminosity in Videos with Application in Lane Detection Systems
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[9]	Multi-Lane Detection and Tracking Using Temporal-Spatial Model and Particle Filtering
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[10]	End to End Structural Prediction and Lane Detection for Autonomous Vehicle
	2020

[11]	Automatic Fire Fighting Apparatus using Image Process of Deep Learning
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[12]	Bayesian curved lane estimation for autonomous driving
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[13]	Vision-Based Pavement Marking Detection–A Case Study
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[14]	Stereovision-based Ridge-Furrow Detection and Tracking System for Autonomous Seeding
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[15]	Lane-Finding Based on Structure Analysis of Lane and Computer Vision
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[16]	Lane Mark and Drivable Area Detection Using a Novel Instance Segmentation Scheme
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[17]	Robust Multi-Lane Detection and Tracking in Temporal-Spatial Based on Particle Filtering
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[18]	Real-time lane markings recognition based on seed-fill algorithm
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[19]	Real-Time Lane Detection and Motion Planning for Autonomous Vehicle
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[20]	Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
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[21]	Ultra-low Complexity Block-based Lane Detection and Departure Warning System
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[22]	Experimental study on scene recognition and multiple road lane marks detection based on machine learning methods
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[23]	Traffic Lane Detection using Fully Convolutional Neural Network
	2018

[24]	Lane detection algorithm based on density clustering and RANSAC
	2018

[25]	Constraint Decision Optimization Model for Safe Autonomous Vehicle Operation
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[26]	Robust Lane Detection Using Multiple Features
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[27]	Lane line detection by using Hough Transform
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[28]	Real-Time Road-Direction Point Detection in Complex Environment
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[29]	Lane marking detection and reconstruction with line-scan imaging data
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[30]	Yapay görü teknikleri ile şerit çizgisi algılama ve sınıflandırma
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[31]	A Hybrid Vision-Map Method for Urban Road Detection
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[32]	Real Time Road Lane Detection with RANSAC and HSV Color Transformation
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[33]	嵌入式車道偏移警示系統設計與實作
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[34]	Automatic road marking recognition for intelligent vehicle systems application
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[35]	ROAD LANE AND EDGE DETECTION WITH GRADIENT AND HOUGH TRANSFORM
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[36]	A Review of Recent Advances in Lane Detection and Departure Warning System
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[37]	嵌入式車道偏移警示系統設計與實作; Design and Implementation of an Embedded Lane Departure Warning System
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[38]	Video Solar Plant Monitoring
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[39]	АЛГОРИТМИЧЕСКОЕ И ПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ АВТОМАТИЧЕСКОГО ОБНАРУЖЕНИЯ И КЛАССИФИКАЦИИ ДЕФЕКТОВ ДОРОЖНОГО ПОКРЫТИЯ …
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[40]	АЛГОРИТМИЧЕСКОЕ И ПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ АВТОМАТИЧЕСКОГО ОБНАРУЖЕНИЯ И КЛАССИФИКАЦИИ ДЕФЕКТОВ ДОРОЖНОГО …
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[41]	Алгоритмическое и программное обеспечение автоматического обнаружения и классификации дефектов дорожного покрытия с помощью метода …
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[42]	Daytime Road Marker Recognition Using Grayscale Histogram and Pixel Values
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[43]	Accurate and robust lane detection based on Dual-View Convolutional Neutral Network
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[44]	A REVIEW ON LANE DETECTION AND TRACKING TECHNIQUES
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[45]	Real-Time Line Marker Detection for Night-Time Blind Spot Monitoring System in Suburb Area
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[46]	Evidential grids with semantic lane information for intelligent vehicles
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[47]	Image Processing based Lane Departure Warning System Using Hough Transform and Euclidean Distance
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[48]	Multi-lane Detection Based on RMFP for Autonomous Vehicle Navigation in Urban Environments
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[49]	Semantic Evidential Lane Grids with Prior Maps for Autonomous Navigation
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[50]	Detección y seguimiento de vehículos mediante análisis de imágenes
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[51]	Performance Assessment of Predictive Lane Boundary Detection for Non-uniformly Illuminated Roadway Driving Assistance
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[52]	차로 수 정보와 텍스쳐 분석을 활용한 주행가능영역 검출 알고리즘
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[53]	Raspberry-pi based real time lane departure warning system using image processing
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[54]	Traversable Region Detection Algorithm using Lane Information and Texture Analysis
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[55]	Detección de vehículos mediante análisis de imágenes
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[56]	Real-time daytime road marker recognition using features vectors and neural network
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[57]	Real time road lane markers detection for intelligent vehicles
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[58]	Lane Detection System Based on a Natural Image Enhancement Technique?
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[59]	Model-Based Robust Lane Detection for Driver Assistance
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[60]	Lane Departure Identification for Advanced Driver Assistance
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[61]	Review on Lane Detection and Departure warning Algorithms
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[62]	Lane Detection System Based on a Natural Image Enhancement Technique
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[63]	Predictive Lane Boundary-Detection in Roads with Non-Uniform Surface Illumination
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[64]	A Review on Lane Detection Andtracking Techniques

[1]	Real-time Lane Detection and Departure caution Gadget for Automobiles based on Raspberry pi 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), 2023 DOI:10.1109/ICAECIS58353.2023.10170258

[2]	Plug-and-Play Multi-class Lane Detection Module Proceedings of the 2023 Workshop on Advanced Multimedia Computing for Smart Manufacturing and Engineering, 2023 DOI:10.1145/3606042.3616457

[3]	Scalable Machine Learning Model for Highway CCTV Feed Real-Time Car Accident and Damage Detection 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2023 DOI:10.1109/3ICT60104.2023.10391543

[4]	Multi-Lane Detection and Tracking Using Temporal-Spatial Model and Particle Filtering IEEE Transactions on Intelligent Transportation Systems, 2022 DOI:10.1109/TITS.2020.3035614

[5]	Multi-Lane Detection and Tracking Using Temporal-Spatial Model and Particle Filtering IEEE Transactions on Intelligent Transportation Systems, 2022 DOI:10.1109/TITS.2020.3035614

[6]	Exposure consistency for lane detection under varied light conditions Journal of Electronic Imaging, 2022 DOI:10.1117/1.JEI.31.3.030502

[7]	Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study Applied Sciences, 2021 DOI:10.3390/app11073152

[8]	Proceedings of International Conference on Machine Intelligence and Data Science Applications Algorithms for Intelligent Systems, 2021 DOI:10.1007/978-981-33-4087-9_24

[9]	Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3083890

[10]	Real-Time Lane Detection by Using Biologically Inspired Attention Mechanism to Learn Contextual Information Cognitive Computation, 2021 DOI:10.1007/s12559-021-09935-5

[11]	Proceedings of the 18th International Conference on Computing in Civil and Building Engineering Lecture Notes in Civil Engineering, 2021 DOI:10.1007/978-3-030-51295-8_81

[12]	Bayesian curved lane estimation for autonomous driving Journal of Ambient Intelligence and Humanized Computing, 2020 DOI:10.1007/s12652-020-01688-7

[13]	Lane-Finding Based on Structure Analysis of Lane and Computer Vision 2019 International Conference on Communication and Electronics Systems (ICCES), 2019 DOI:10.1109/ICCES45898.2019.9002280

[14]	Real-time lane markings recognition based on seed-fill algorithm Proceedings of the International Conference on Information and Communication Technology - ICICT '19, 2019 DOI:10.1145/3321289.3321306

[15]	Robust Multi-Lane Detection and Tracking in Temporal-Spatial Based on Particle Filtering SAE Technical Paper Series, 2019 DOI:10.4271/2019-01-0885

[16]	Lane Mark and Drivable Area Detection Using a Novel Instance Segmentation Scheme 2019 IEEE/SICE International Symposium on System Integration (SII), 2019 DOI:10.1109/SII.2019.8700359

[17]	Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), 2019 DOI:10.1109/MMSP.2019.8901686

[18]	Ultra-Low Complexity Block-Based Lane Detection and Departure Warning System IEEE Transactions on Circuits and Systems for Video Technology, 2019 DOI:10.1109/TCSVT.2018.2805704

[19]	Real-time lane markings recognition based on seed-fill algorithm Proceedings of the International Conference on Information and Communication Technology, 2019 DOI:10.1145/3321289.3321306

[20]	Review on: Real Time Lane Departure Awareness System & Maintenance in Reducing Road Accidents 2018 International Conference on Information , Communication, Engineering and Technology (ICICET), 2018 DOI:10.1109/ICICET.2018.8533841

[21]	Robust Lane Detection Using Multiple Features 2018 IEEE Intelligent Vehicles Symposium (IV), 2018 DOI:10.1109/IVS.2018.8500431

[22]	Lane detection algorithm based on density clustering and RANSAC 2018 Chinese Control And Decision Conference (CCDC), 2018 DOI:10.1109/CCDC.2018.8407261

[23]	Constraint Decision Optimization Model for Safe Autonomous Vehicle Operation 2018 IEEE Intelligent Vehicles Symposium (IV), 2018 DOI:10.1109/IVS.2018.8500570

[24]	Real-Time Road-Direction Point Detection in Complex Environment IEICE Transactions on Information and Systems, 2018 DOI:10.1587/transinf.2017EDP7266

[25]	Lane Marking Detection and Reconstruction with Line-Scan Imaging Data Sensors, 2018 DOI:10.3390/s18051635

[26]	Traffic Lane Detection using Fully Convolutional Neural Network 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2018 DOI:10.23919/APSIPA.2018.8659684

[27]	A review of recent advances in lane detection and departure warning system Pattern Recognition, 2018 DOI:10.1016/j.patcog.2017.08.014

[28]	A Hybrid Vision-Map Method for Urban Road Detection Journal of Advanced Transportation, 2017 DOI:10.1155/2017/7090549

[29]	Automatic road marking recognition for intelligent vehicle systems application Advances in Mechanical Engineering, 2017 DOI:10.1177/1687814017706267

[30]	Video Solar Plant Monitoring 2017 International Renewable and Sustainable Energy Conference (IRSEC), 2017 DOI:10.1109/IRSEC.2017.8477387

[31]	Traversable Region Detection Algorithm using Lane Information and Texture Analysis Journal of Korea Multimedia Society, 2016 DOI:10.9717/kmms.2016.19.6.979

[32]	Accurate and robust lane detection based on Dual-View Convolutional Neutral Network 2016 IEEE Intelligent Vehicles Symposium (IV), 2016 DOI:10.1109/IVS.2016.7535517

[33]	Performance Assessment of Predictive Lane Boundary Detection for Non-uniformly Illuminated Roadway Driving Assistance 2016 13th Conference on Computer and Robot Vision (CRV), 2016 DOI:10.1109/CRV.2016.52

[34]	Semantic evidential lane grids with prior maps for autonomous navigation 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016 DOI:10.1109/ITSC.2016.7795860

[35]	Semantic evidential lane grids with prior maps for autonomous navigation 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016 DOI:10.1109/ITSC.2016.7795860

[36]	Real-time daytime road marker recognition using features vectors and neural network 2015 IEEE Conference on Sustainable Utilization And Development In Engineering and Technology (CSUDET), 2015 DOI:10.1109/CSUDET.2015.7446223

[37]	Model-Based Robust Lane Detection for Driver Assistance Journal of Korea Multimedia Society, 2014 DOI:10.9717/kmms.2014.17.6.655

[38]	Lane Departure Identification for Advanced Driver Assistance IEEE Transactions on Intelligent Transportation Systems, 2014 DOI:10.1109/TITS.2014.2347400

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	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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