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Truck Image Dataset
- data.niaid.nih.gov
Updated Mar 4, 2023
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EmailClick to copy linkLink copiedCiteAndre Luiz Cunha (2023). Truck Image Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5744736Dataset updatedMar 4, 2023Dataset provided byLeandro Arab Marcomini
Andre Luiz CunhaLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionCollection of annotated truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.
The dataset includes 727 cropped images of trucks, taken with three different cameras, on five different locations.
727 images
Format: JPG
Resolution: 1920xVarious, 96dpi, 24bits
Naming pattern: _--.jpg
All annotated objects were created with LabelMe, and saved in JSON files for each image. For more information about the annotation format, please refer to the LabelMe documentation.
Annotated objects are all related to truck axles, in 4 categories, Truck, Axle, Tandem, Tridem. Tandem is a double axle composition, and tridem is a triple axle composition. The number of objects in each category is as follows:
Truck: 736
Axle: 2711
Tandem: 809
Tridem: 130
If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!
Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).
Truck Image Dataset
- zenodo.org
zipUpdated Mar 3, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteLeandro Arab Marcomini; Leandro Arab Marcomini; Andre Luiz Cunha; Andre Luiz Cunha (2023). Truck Image Dataset [Dataset]. http://doi.org/10.5281/zenodo.5744737zipAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.5744737Dataset updatedMar 3, 2023AuthorsLeandro Arab Marcomini; Leandro Arab Marcomini; Andre Luiz Cunha; Andre Luiz CunhaLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionCollection of truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.
The dataset includes 725 cropped images of trucks, taken with three different cameras, on five different locations.
- 725 images
- Format: JPG
- Resolution: 1920xVarious, 96dpi, 24bits
- Naming pattern:
If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!
Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).
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FacebookTwitterEmailAndre Luiz Cunha
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Collection of annotated truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.
The dataset includes 727 cropped images of trucks, taken with three different cameras, on five different locations.
727 images
Format: JPG
Resolution: 1920xVarious, 96dpi, 24bits
Naming pattern: _--.jpg
All annotated objects were created with LabelMe, and saved in JSON files for each image. For more information about the annotation format, please refer to the LabelMe documentation.
Annotated objects are all related to truck axles, in 4 categories, Truck, Axle, Tandem, Tridem. Tandem is a double axle composition, and tridem is a triple axle composition. The number of objects in each category is as follows:
Truck: 736
Axle: 2711
Tandem: 809
Tridem: 130
If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!
Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).