Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey
Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4832 |
| _version_ | 1797598383469232128 |
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| author | Michael Abebe Berwo Asad Khan Yong Fang Hamza Fahim Shumaila Javaid Jabar Mahmood Zain Ul Abideen Syam M.S. |
| author_facet | Michael Abebe Berwo Asad Khan Yong Fang Hamza Fahim Shumaila Javaid Jabar Mahmood Zain Ul Abideen Syam M.S. |
| author_sort | Michael Abebe Berwo |
| collection | DOAJ |
| description | Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years. |
| first_indexed | 2024-03-11T03:20:24Z |
| format | Article |
| id | doaj.art-6e3e8b06a9d047a281b2299a0a60e0dd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:24Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6e3e8b06a9d047a281b2299a0a60e0dd2023-11-18T03:13:16ZengMDPI AGSensors1424-82202023-05-012310483210.3390/s23104832Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A SurveyMichael Abebe Berwo0Asad Khan1Yong Fang2Hamza Fahim3Shumaila Javaid4Jabar Mahmood5Zain Ul Abideen6Syam M.S.7School of Information and Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Information and Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronics and Information, Tongji University, Shanghai 200070, ChinaSchool of Electronics and Information, Tongji University, Shanghai 200070, ChinaSchool of Information and Engineering, Chang’an University, Xi’an 710064, ChinaResearch Institute of Automotive Engineering, Jiangsu University, Zhenjiang 212013, ChinaIOT Research Center, Shenzhen University, Shenzhen 518060, ChinaDetecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.https://www.mdpi.com/1424-8220/23/10/4832deep learningvehicle detection and classificationCNNactivation functionloss function |
| spellingShingle | Michael Abebe Berwo Asad Khan Yong Fang Hamza Fahim Shumaila Javaid Jabar Mahmood Zain Ul Abideen Syam M.S. Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey Sensors deep learning vehicle detection and classification CNN activation function loss function |
| title | Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey |
| title_full | Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey |
| title_fullStr | Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey |
| title_full_unstemmed | Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey |
| title_short | Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey |
| title_sort | deep learning techniques for vehicle detection and classification from images videos a survey |
| topic | deep learning vehicle detection and classification CNN activation function loss function |
| url | https://www.mdpi.com/1424-8220/23/10/4832 |
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