Vehicle image datasets for image classification
Vehicle image recognition is a critical research area with diverse traffic management, surveillance, and autonomous driving systems applications. Accurately classifying and identifying vehicles from images play a crucial role in these domains. This work presents two vehicle image datasets: the vehic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924001057 |
| _version_ | 1797256370396856320 |
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| author | Narong Boonsirisumpun Emmanuel Okafor Olarik Surinta |
| author_facet | Narong Boonsirisumpun Emmanuel Okafor Olarik Surinta |
| author_sort | Narong Boonsirisumpun |
| collection | DOAJ |
| description | Vehicle image recognition is a critical research area with diverse traffic management, surveillance, and autonomous driving systems applications. Accurately classifying and identifying vehicles from images play a crucial role in these domains. This work presents two vehicle image datasets: the vehicle type image dataset version 2 (VTID2) and the vehicle make image dataset (VMID). The VTID2 Dataset comprises 4,356 images of Thailand's five most used vehicle types, which enhances diversity and reduces the risk of overfitting problems. This expanded dataset offers a more extensive and varied collection for robust model training and evaluation. This dataset will be valuable for researchers focusing on vehicle image recognition tasks. With an emphasis on sedans, hatchbacks, pick-ups, SUVs, and other vehicles, the dataset allows for developing and evaluating algorithms that accurately classify different types of vehicles. The VMID Dataset contains 2,072 images of logos (called vehicle make) from eleven prominent vehicle brands in Thailand. The proposed dataset will facilitate the development of computer vision algorithms and the evaluation of learning algorithm model performance metrics. These two datasets provide valuable resources to the research community that will foster possible research advancements in vehicle recognition, vehicle logo detection or localization, and vehicle segmentation, contributing to the development of intelligent transportation systems. |
| first_indexed | 2024-03-08T05:14:28Z |
| format | Article |
| id | doaj.art-c559f7280ce746a5be5d30b557599a1f |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-24T22:20:40Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-c559f7280ce746a5be5d30b557599a1f2024-03-20T06:09:49ZengElsevierData in Brief2352-34092024-04-0153110133Vehicle image datasets for image classificationNarong Boonsirisumpun0Emmanuel Okafor1Olarik Surinta2Department of Computer Science, Loei Rajabhat University, Loei 42000, ThailandSDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaMulti-agent Intelligent Simulation, Laboratory (MISL) Research Unit, Department of Information Technology, Faculty of Informatics, Mahasarakham University, Mahasarakham, 44150 Thailand; Corresponding author.Vehicle image recognition is a critical research area with diverse traffic management, surveillance, and autonomous driving systems applications. Accurately classifying and identifying vehicles from images play a crucial role in these domains. This work presents two vehicle image datasets: the vehicle type image dataset version 2 (VTID2) and the vehicle make image dataset (VMID). The VTID2 Dataset comprises 4,356 images of Thailand's five most used vehicle types, which enhances diversity and reduces the risk of overfitting problems. This expanded dataset offers a more extensive and varied collection for robust model training and evaluation. This dataset will be valuable for researchers focusing on vehicle image recognition tasks. With an emphasis on sedans, hatchbacks, pick-ups, SUVs, and other vehicles, the dataset allows for developing and evaluating algorithms that accurately classify different types of vehicles. The VMID Dataset contains 2,072 images of logos (called vehicle make) from eleven prominent vehicle brands in Thailand. The proposed dataset will facilitate the development of computer vision algorithms and the evaluation of learning algorithm model performance metrics. These two datasets provide valuable resources to the research community that will foster possible research advancements in vehicle recognition, vehicle logo detection or localization, and vehicle segmentation, contributing to the development of intelligent transportation systems.http://www.sciencedirect.com/science/article/pii/S2352340924001057Vehicle type imageVehicle make imageVehicle logoThai vehicle imageVehicle image recognitionImage classification |
| spellingShingle | Narong Boonsirisumpun Emmanuel Okafor Olarik Surinta Vehicle image datasets for image classification Data in Brief Vehicle type image Vehicle make image Vehicle logo Thai vehicle image Vehicle image recognition Image classification |
| title | Vehicle image datasets for image classification |
| title_full | Vehicle image datasets for image classification |
| title_fullStr | Vehicle image datasets for image classification |
| title_full_unstemmed | Vehicle image datasets for image classification |
| title_short | Vehicle image datasets for image classification |
| title_sort | vehicle image datasets for image classification |
| topic | Vehicle type image Vehicle make image Vehicle logo Thai vehicle image Vehicle image recognition Image classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924001057 |
| work_keys_str_mv | AT narongboonsirisumpun vehicleimagedatasetsforimageclassification AT emmanuelokafor vehicleimagedatasetsforimageclassification AT olariksurinta vehicleimagedatasetsforimageclassification |