Truck Image Dataset

<p>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 S&atilde;o 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:&nbsp;<a href="https://www.researchgate.net/lab/Andre-Luiz-Cunha-Lab">Researchgate Lab Page</a>,&nbsp;OrcID Profiles, or&nbsp;<a href="https://github.com/labITS-stt-eesc">ITS Lab page on Github</a>.</p> <p>The dataset includes 1053 cropped images of trucks, with mixed real world trucks and synthetic trucks from Euro Truck Simulator 2.</p> <p>727 images were taken with three different cameras, on five different locations.</p> <ul> <li>727&nbsp;images</li> <li>Format: JPG</li> <li>Resolution: 1920xVarious, 96dpi, 24bits</li> <li>Naming pattern: &lt;video_name&gt;_&lt;color|gray&gt;-&lt;Region_of_Interest_ID&gt;-&lt;truck_ID&gt;.jpg</li> </ul> <p>326 images were collected from <a href="https://truckersmp.com/" target="_blank" rel="noopener">Trucker's MP website</a>.</p> <ul> <li>326 images</li> <li>Format: JPG</li> <li>Resolution: 1920xVarious, 96dpi, 24bits</li> <li>Naming Pattern: &lt;HEXID&gt;.jpg</li> </ul> <p>All annotated objects were created with <a href="https://github.com/wkentaro/labelme">LabelMe</a>, and saved in JSON files for each image. For more information about the annotation format, please refer to the LabelMe&nbsp;documentation.</p> <p>Annotated objects are all related to truck axles, in 4 categories, Truck, Axle, Tandem, Tridem. Tandem is&nbsp;a double axle composition, and tridem is a triple axle composition. The number of objects in each category is as follows:&nbsp;</p> <ul> <li>Truck: 1053&nbsp;</li> <li>Axle: 3927</li> <li>Tandem: 1172</li> <li>Tridem: 188</li> </ul> <p>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&nbsp;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!</p> <p>Marcomini, Leandro Arab, and Andr&eacute; Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks."&nbsp;<em>arXiv preprint <a href="https://arxiv.org/abs/2204.01868">arXiv:2204.01868</a></em>&nbsp;(2022).</p> <p>&nbsp;</p>