research-article

A new architecture based on convolutional neural networks (CNN) for assisting the driver in fog environment

Authors:

Ben Ahmed Mohamed,

Boudhir Anouar AbdelhakimAuthors Info & Claims

SCA '18: Proceedings of the 3rd International Conference on Smart City Applications

Article No.: 85, Pages 1 - 5

https://doi.org/10.1145/3286606.3286862

Published: 10 October 2018 Publication History

Abstract

Driver Assistance Systems (ADAS) are designed to assist the driver and improve road safety. For this, various sensors are generally embedded in vehicles to alert the driver in case of danger present on the road. Unfortunately, the performance of such systems degrades in the presence of adverse weather conditions. In addition, eliminating the fog of a single image captured by a camera is a very difficult and ill-posed phenomenon in Advanced Driver Assistance Systems (ADAS). Recent developments in the field of deep learning have allowed researchers to build relevant models using various tools available. We propose in this paper a new architecture based on fast R-CNN for the detection of objects in fogged images, and a convolutional neuron network (CNN) is designed on the basis of a reformulated model of atmospheric diffusion for fog elimination to restore the sharp image.

References

[1]

R. T. Tan. Visibility in bad weather from a single image.In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1--8. IEEE, 2008.

[2]

S.G. Narasimhan and S.K. Nayar, "Vision and the atmosphere," Int'l J. Computer Vision, vol.48, no.3, pp.233--254, 2002.

Digital Library

[3]

M. Pavlic, H. Belzner, G. Rogoll, and S. Ilic, "Image based fog detection in vehicles," IEEE Intelligent Vehicles Symposium, pp.1132--1137, June 2012.

[4]

S. Alami, A. Ezzine, and F. Elhassouni, "Local fog detection based on saturation and RGB-correlation," Proc. IEEE International Con- ference Computer Graphics, Imaging and Visualization, pp.1--5, March 2016

[5]

E. J. McCartney. Optics of the atmosphere: scattering by molecules and particles. New York, John Wiley and Sons, Inc., 1976. 421 p., 1976.

[6]

S. G. Narasimhan and S. K. Nayar. Chromatic framework for vision in bad weather. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, vol- ume 1, pages 598--605. IEEE, 2000.

[7]

S. G. Narasimhan and S. K. Nayar. Vision and the atmosphere.

Digital Library

[8]

R. Fattal. Single image dehazing. ACM transactions on graphics (TOG), 27(3):72, 2008.

Digital Library

[9]

K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12):2341--2353, 2011.

Digital Library

[10]

K. Tang, J. Yang, and J. Wang. Investigating haze-relevant features in a learning framework for image dehazing. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2995--3000, 2014.

Digital Library

[11]

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan. Effi- cient image dehazing with boundary constraint and contex- tual regularization. In Proceedings of the IEEE international conference on computer vision, pages 617--624, 2013.

Digital Library

[12]

M. Sulami, I. Glatzer, R. Fattal, and M. Werman. Automatic recovery of the atmospheric light in hazy images. In Compu- tational Photography (ICCP), 2014 IEEE International Con- ference on, pages 1--11. IEEE, 2014.

[13]

Q. Zhu, J. Mai, and L. Shao. A fast single image haze re- moval algorithm using color attenuation prior. IEEE Trans- actions on Image Processing, 24(11):3522--3533, 2015.

Digital Library

[14]

F.Hussain and J.Jeong Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks Hindawi Publishing Corporation Journal of SensorsVolume 2016, Article ID 3894832, 9 pages

[15]

Chongyi Li Jichang Guo, Fatih Porikli, Huazhu Fu, and Yanwei Pang. A Cascaded Convolutional Neural Network for Single Image Dehazing. accepted by IEEE ACCESS, Computer Vision and Pattern Recognition (cs.CV), Submitted on 21 Mar 2018

[16]

Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu and Dan Feng. An All-in-One Network for Dehazing and Beyond. journal of latex class files, vol. 14, no. 8, august 2015

[17]

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang. Single image dehazing via multi-scale convolutional neural networks. In European Conference on Computer Vision, pages 154--169. Springer, 2016.

[18]

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11), 2016.

Digital Library

[19]

Y. LeCun, K. Kavukvuoglu, and C. Farabet. Convolutional networks and applications in vision. In Circuits and Systems, International Symposium on, pages 253--256, 2010.

[20]

LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278--2324. {CrossRef}

[21]

{YY} R. Girshick, "Fast R-CNN," in IEEE International Conference onComputer Vision (ICCV), 2015.

Digital Library

Cited By

Jain VChakrabarti PMitolo MLeonowicz ZJasinski MVinogradov ABolshev V(2022)A Power-Efficient Multichannel Low-Pass Filter Based on the Cascaded Multiple Accumulate Finite Impulse Response (CMFIR) Structure for Digital Image ProcessingCircuits, Systems, and Signal Processing10.1007/s00034-022-01960-541:7(3864-3881)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s00034-022-01960-5
Yang LGong XWu ZHan YHe LLi F(2021)Dark-Channel Mixed Attention Based Neural Networks for Smoke Detection in Fog EnvironmentAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3480403(691-696)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3480403

Recommendations

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification
Pattern Recognition. ICPR International Workshops and Challenges
Abstract
The latest generation cars are often equipped with advanced driver assistance systems, usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver by leveraging several levels of automation. ...
Characteristic of Freeway Traffic Accidents in Fog in Liaoning Province
ICISEM '13: Proceedings of the 2013 International Conference on Information System and Engineering Management

The heavy fog is a main cause to induce traffic accidents. On the basis of analyzing traffic accidents data of 15 freeways in fog in Liaoning province, the time distribution, accident characteristics and accident type are obtained to explore the ...
Mining consumer complaints to identify unsuccessful interactions with advanced driver assistance systems
AutomotiveUI '19: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings

Advanced driver assistance systems, which warn drivers of danger of an impending collision or temporarily control the vehicle's speed and/or direction under limited circumstances to assist the driver, have the potential to prevent large numbers of motor ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

SCA '18: Proceedings of the 3rd International Conference on Smart City Applications

October 2018

580 pages

ISBN:9781450365628

DOI:10.1145/3286606

Editors:
Ben Ahmed Mohamed
UAE University, FST of Tangier, Morocco
,
Boudhir Anouar Abdelhakim
UAE University, FST of Tangier, Morocco
,
Younes Ali
UAE University, FS of Tetouan, Morocco

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

SCA '18

SCA '18: 3rd International Conference on Smart City Applications

October 10 - 11, 2018

Tetouan, Morocco

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
152
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jain VChakrabarti PMitolo MLeonowicz ZJasinski MVinogradov ABolshev V(2022)A Power-Efficient Multichannel Low-Pass Filter Based on the Cascaded Multiple Accumulate Finite Impulse Response (CMFIR) Structure for Digital Image ProcessingCircuits, Systems, and Signal Processing10.1007/s00034-022-01960-541:7(3864-3881)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s00034-022-01960-5
Yang LGong XWu ZHan YHe LLi F(2021)Dark-Channel Mixed Attention Based Neural Networks for Smoke Detection in Fog EnvironmentAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3480403(691-696)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3480403

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents