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Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network.

Sangwon Kim1, Jaeyeal Nam2, Byoungchul Ko3

  • 1Department of Computer Engineering, Keimyung University, Daegu 42601, Korea. swkim@stu.kmu.ac.kr.

Sensors (Basel, Switzerland)
|October 17, 2019
PubMed
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This study introduces a lightweight neural network (L-ENet) for real-time depth estimation from single images. The L-ENet algorithm accurately generates depth maps, outperforming existing single-image methods.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Depth estimation is vital in computer vision but traditional multi-image methods are complex and hardware solutions are costly.
  • Existing single-image methods using machine learning offer alternatives but require further optimization for real-time applications.

Purpose of the Study:

  • To develop a software-based algorithm for real-time depth map generation from single images.
  • To propose an optimized lightweight efficient neural network (L-ENet) that eliminates the need for specialized hardware.

Main Methods:

  • Utilized pixel-wise prediction with ordinal depth range classification due to the continuous and potentially ambiguous nature of depth values.
  • Employed various convolution techniques to extract dense feature maps while reducing network parameters through layer reduction.
Keywords:
convolutional neural networkdepth estimationlightweight efficient neural networkmodelordinal regressionsingle image

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  • Developed a lightweight efficient neural network (L-ENet) optimized for speed and efficiency.

    • Main Results:

    • The L-ENet algorithm successfully generates accurate depth maps from single images in real time.
    • The proposed method produces depth values with minimal errors, closely matching ground truth.
    • Comparative experiments demonstrate superior performance over state-of-the-art single-image depth estimation algorithms.

    Conclusions:

    • The L-ENet algorithm provides an efficient and accurate solution for single-image depth estimation.
    • This approach overcomes the limitations of traditional multi-image and hardware-based methods.
    • The study highlights the potential of optimized lightweight neural networks for real-time computer vision applications.