Lane and Curve Detection using Deep Learning

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Description

Lane and Curve Detection using Deep Learning

Abstract

With the development of automation and electrification, autonomous robotics and vehicles have a wide range of use in space science and self-driving cars. An important part of autonomous vehicles is the navigation system. In the past decades using vision-based systems guidance systems became more popular. By taking the tire and road interaction from the vehicles automatically the terrain has to be classified these type of technique was used in the rovers and pathfinder now it came to the automobiles. In this project, deep learning technology is used to detect curved paths in autonomous vehicles. In this paper, a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labeling toolbox for deep learning is used to detect the curved path in an autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy are achieved in detecting path.


System Analysis

 Existing Systems

  • Principal Component Analysis
  • DCT  and shape features
  • SVM classifier 

Proposed Method

  •  GLCM Features
  • NN Classifier

Advantages

  • The segmentation algorithm Proves to be simple and effective
  • The greyscale Co-occurrence matrix performed well in NN
  • Better texture and edge representation 
  • Segmentation provides better clustering efficiency

Block Diagram 

 

Lane and Curve Detection using Deep Learning
Lane and Curve Detection using Deep Learning

Requirement Specifications

 Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

Software Requirement

  • MATLAB 2014a

REFERENCES

  • [1] Upadhyay, Y. and Wasson, V. 2014. “Analysis of Liver MR Images for Cancer Detection using Genetic Algorithm”. International Journal of Engineering Research and General Science. Vol.2, No.4, PP: 730-737.
  •  [2] Kumar, P. Bhalerao, S. 2014. “Detection of Tumor in Liver Using Image Segmentation and Registration Technique”. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). Vo.9, No.2, PP: 110-115.
  •  [3] Selle, D.; Spindler, W.; Preim, B. and Peitgen, H. O. 2000. “Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning”. PP: 1-21. 
  • [4] Zimmer, C. and Olivo-Marin, J. C. 2005. “Coupled Parametric Active Contours”. Transactions on Pattern Analysis and Machine Intelligence. Vol.27, No.11, PP: 1838-1841.
  •  [5] Chitra, S. and Balakrishnan, G. 2012. “Comparative Study for Two-Color Spaces HSCbCr and YCbCr in Skin Color Detection”. Applied Mathematical Sciences. Vol.6, No.85, PP: 4229 – 4238.

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