Object detection: review and improvement based on deep learning

Tingyu Wang

doi:10.54254/2755-2721/13/20230701

Research Article

Open access

Published on 23 October 2023

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Wang,T. (2023). Object detection: review and improvement based on deep learning. Applied and Computational Engineering,13,1-6.

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Object detection: review and improvement based on deep learning

Tingyu Wang *^,1,

¹ Tianjin University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/13/20230701

Abstract

In recent years, with the quantum leap in deep learning, self-driving vehicle as one of its applications has been gaining tremendously increasing popularity as well as making a multitude of achievements. Object detection, which has made significant contribution to driver-less vehicle, has had been applied to a tremendously wide range of fields. However, reports relevant to automatic vehicle stating that accidents are caused by Automatic driving technology, present problems pointing out that existing target detection algorithms, which are already fairly well reliable, can probably be interfered by adverse conditions such as high temperature, raise dust and transmission loss, and be not capable of providing precise output. This paper recaps on these previous classic algorithms, and their large-scale application domain. Meanwhile, this paper presents improvements focusing on enhancing the robustness of these algorithms to overcome these problems caused by adverse conditions and improve the accuracy. Thus these improvements could augment the security of these driver-less vehicles, and eventually reduce traffic accident mortality relative to self-driving vehicles and safeguard road safety, and may potentially benefit to further research.

Keywords

target detection, deep learning, computer vision

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Cite this article

Wang,T. (2023). Object detection: review and improvement based on deep learning. Applied and Computational Engineering,13,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-017-2(Print) / 978-1-83558-018-9(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.13

ISSN：2755-2721(Print) / 2755-273X(Online)

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