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 1053 cropped images of trucks, with mixed real world trucks and synthetic trucks from Euro Truck Simulator 2. 727 images were taken with three different cameras, on five different locations. 727 images Format: JPG Resolution: 1920xVarious, 96dpi, 24bits Naming pattern: _--.jpg 326 images were collected from Trucker's MP website. 326 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: 1053 Axle: 3927 Tandem: 1172 Tridem: 188 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).

v2.0.0 - Added annotation files for every image; - Four new objects annotated: Truck, Axle, Tandem, Tridem; v1.0.0 - Added base truck dataset;