RoadTrafficMARKS: Dataset of 10057 images (256x256 pixels) of Curved arrow, Straight arrow, Pedestrian crossing, Straight-right merge arrow, Merge arrow, Left arrow, Yield, Stop, Right arrow, Straight-left merge arrow, Speed limit and Straight-right-left merge arrow markings and their BoundingBox labels

The dataset consists of 10057 PNG images (256x256 pixels) of high resolution aerial orthoimages taggged with twelve classes of traffic signals: (1) Curved arrow, (2) Straight arrow, (3) Pedestrian crossing, (4) Straight-right merge arrow, (5) Merge arrow, (6) Left arrow, (7) Yield, (8) Stop, (9) Right arrow, (10) Straight-left merge arrow, (11) Speed limit and (12) Straight-right-left merge arrow markings, together with their corresponding Bounding Boxes. The dataset has been created in the framework of the SROADEX project to train an identification process based on artificial neural networks.

The dataset was created by manually tagging the twelve class of marks on orthoimage tiles of 256x256 pixels with the LabelMe tool. After the semantic labeling (manual digitalization of the contour of the signals), a transformation and random splitting process has been carried to prepare the data for the neural networks training. It resulted in 80% of the images for training (8031), 10% for validation (1000) and 10% for testing (1021).

The next table presents the number of images of each class on the "train", "valid" and "test" sets.