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Hybrid RESNET and regional convolution neural network for accident estimation

Djenouri, Youcef; Srivastava, Gautam; Djenouri, Djamel; Belhadi, Asma; Jerry, Chun-Wei Lin

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Authors

Youcef Djenouri

Gautam Srivastava

Asma Belhadi

Chun-Wei Lin Jerry



Abstract

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT extractor and a Chinese restaurant process is used to remove noise. The extended region-based convolution neural network is then applied to identify the closest vehicles to the given driver. The residual network will benefit from the vehicle detection process to make a binary classification on whether the current road state might cause an accident or not. Finally, we propose a novel optimization model for optimizing hyper-parameters in deep learning methodologies by using evolutionary computation. The proposed solution has been tested using benchmark vehicle detection and accident estimation datasets. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy, where the proposed solution has more than 5% of improved accident estimation rate compared to the conventional methods.

Citation

Djenouri, Y., Srivastava, G., Djenouri, D., Belhadi, A., & Jerry, C. L. (2022). Hybrid RESNET and regional convolution neural network for accident estimation. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25335-25344. https://doi.org/10.1109/TITS.2022.3165156

Journal Article Type Article
Acceptance Date Apr 1, 2022
Online Publication Date Apr 14, 2022
Publication Date 2022-12
Deposit Date Apr 1, 2022
Publicly Available Date May 15, 2022
Journal IEEE Transactions on Intelligent Transportation Systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 12
Pages 25335-25344
DOI https://doi.org/10.1109/TITS.2022.3165156
Keywords Index Terms-Deep Learning; Vehicle Detection; Accident Estimation; Region Convolution Neural Network; Residual Network; Outlier Detection; Hyper-parameters Optimization; smart roads
Public URL https://uwe-repository.worktribe.com/output/9278828

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Copyright Statement
This is the author’s accepted manuscript of the article ‘Djenouri, Y., Srivastava, G., Djenouri, D., Belhadi, A., & Jerry, C. L. (2022). Hybrid RESNET and regional convolution neural network for accident estimation. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25335-25344’.

The final published version is available here: https://ieeexplore.ieee.org/document/9757754

https://doi.org/10.1109/TITS.2022.3165156.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.




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