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An end-to-end stereo matching algorithm based on improved convolutional neural network.

Yan Liu1, Bingxue Lv1, Yuheng Wang1

  • 1College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 45000, China.

Mathematical Biosciences and Engineering : MBE
|December 31, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient end-to-end stereo matching algorithm using a "downsize" convolutional neural network (CNN) for autonomous driving. The method enhances depth accuracy and reduces runtime for real-world applications.

Keywords:
binocular visionconvolutional neural networkimage sensorstereo matching

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Autonomous Systems

Background:

  • Stereo matching is crucial for depth perception in autonomous driving.
  • Existing deep learning methods face efficiency challenges in real-world scenarios.
  • Network layer size impacts feature parameter training and overall efficiency.

Purpose of the Study:

  • To develop an efficient end-to-end stereo matching algorithm for autonomous driving.
  • To improve the accuracy and reduce the runtime of depth estimation.
  • To address the efficiency limitations of current stereo matching techniques.

Main Methods:

  • Utilized a "downsize" convolutional neural network (CNN) for processing road images.
  • Employed a "downsize" full-connection layer with network optimization for enhanced accuracy.
  • Implemented an improved loss function for better sample similarity approximation.

Main Results:

  • Achieved reduced loss function errors of 2.62% on KITTI 2012 and 3.26% on KITTI 2015.
  • Demonstrated improved algorithm accuracy and reduced runtime.
  • Showcased the ability to generate dense disparity maps for binocular vision systems.

Conclusions:

  • The proposed end-to-end stereo matching algorithm is effective for autonomous driving.
  • The method offers a compressed network size with superior performance compared to prior work.
  • The generated depth information is suitable for binocular vision systems in autonomous vehicles.