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Accelerating Colorizer of Shaded Image for Autonomous Driving in Resource-Constrained SoC (poster)

Authors:

Donghee Ha,

Jinse Kwon,

Hyungshin KimAuthors Info & Claims

MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

Page 636

https://doi.org/10.1145/3307334.3328664

Published: 12 June 2019 Publication History

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Abstract

Image-based convolutional neural network(CNN) algorithms are spreading across a variety of applications. In particular, an autonomous vehicle recognizes objects and the surrounding situation using the CNN models. CNN models for image classification are trained using clear image dataset, so they are not robust to grayscale images or noise-intensive data. Therefore, there is a risk of an accident because the quality of the input image drops rapidly during night driving. The region that is revealed by the headlight can have colors, but in the shaded area it has a brightness that is not enough to get color values. We intend to increase the safety of autonomous driving by coloring this region of interest(ROI).

Reference

[1]

S. Iizuka, E. Simo-Serra, and H. Ishikawa. Let there be color!: Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graph., 35(4):110:1--110:11, July 2016.

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Accelerating Colorizer of Shaded Image for Autonomous Driving in Resource-Constrained SoC (poster)
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Mobile devices

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Published In

MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

June 2019

736 pages

ISBN:9781450366618

DOI:10.1145/3307334

General Chairs:
Junehwa Song
KAIST, South Korea
,
Minkyong Kim
Samsung Electronics
,
Program Chairs:
Nicholas D. Lane
University of Oxford & Samsung AI
,
Rajesh K. Balan
Singapore Management University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 12 June 2019

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MobiSys '19: The 17th Annual International Conference on Mobile Systems, Applications, and Services

June 17 - 21, 2019

Seoul, Republic of Korea

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Numerical Analysis of Tractor Accidents using Driving Simulator for Autonomous Driving Tractor

A deep learning based image enhancement approach for autonomous driving at night

Autonomous Driving: Investigating the Feasibility of Bimodal Take-Over Requests

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