A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems
An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, nu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/6/5/134 |
| _version_ | 1797572295332462592 |
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| author | Danesh Shokri Christian Larouche Saeid Homayouni |
| author_facet | Danesh Shokri Christian Larouche Saeid Homayouni |
| author_sort | Danesh Shokri |
| collection | DOAJ |
| description | An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7). |
| first_indexed | 2024-03-10T20:54:12Z |
| format | Article |
| id | doaj.art-ed42d83bac70476882d1807da80c293d |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-10T20:54:12Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-ed42d83bac70476882d1807da80c293d2023-11-19T18:07:18ZengMDPI AGSmart Cities2624-65112023-10-01652982300410.3390/smartcities6050134A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation SystemsDanesh Shokri0Christian Larouche1Saeid Homayouni2Département des Sciences Géomatiques, Université Laval, Québec, QC G1V 0A6, CanadaDépartement des Sciences Géomatiques, Université Laval, Québec, QC G1V 0A6, CanadaCentre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1V 0A6, CanadaAn Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7).https://www.mdpi.com/2624-6511/6/5/134Intelligent Transportation System (ITS)road traffic surveillancevehicle detection and localizationdeep neural network structureshighway camerassmart cities |
| spellingShingle | Danesh Shokri Christian Larouche Saeid Homayouni A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems Smart Cities Intelligent Transportation System (ITS) road traffic surveillance vehicle detection and localization deep neural network structures highway cameras smart cities |
| title | A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems |
| title_full | A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems |
| title_fullStr | A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems |
| title_full_unstemmed | A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems |
| title_short | A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems |
| title_sort | comparative analysis of multi label deep learning classifiers for real time vehicle detection to support intelligent transportation systems |
| topic | Intelligent Transportation System (ITS) road traffic surveillance vehicle detection and localization deep neural network structures highway cameras smart cities |
| url | https://www.mdpi.com/2624-6511/6/5/134 |
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