Dataset: Variable Message Signal Annotated Images for Object Detection

Enrique Puertas, Gonzalo De-Las-Heras, Javier Sánchez-Soriano and Javier Fernández-Andrés
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Enrique Puertas: Department of Science, Computing and Technology, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
Gonzalo De-Las-Heras: SICE Canada Inc., Toronto, ON M4P 1G8, Canada
Javier Sánchez-Soriano: Higher Polytechnic School, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain
Javier Fernández-Andrés: Department of Engineering, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain

Data, 2022, vol. 7, issue 4, 1-7

Abstract: This publication presents a dataset consisting of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Additionally, a CSV file is attached with information regarding the geographic position, the folder where the image is located and the text in Spanish. This can be used to train supervised learning computer vision algorithms such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition and labeling and its specifications are detailed. The dataset constitutes 1216 instances, 888 positives and 328 negatives, in 1152 jpg images with a resolution of 1280 × 720 pixels. These are divided into 756 real images and 756 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

Keywords: variable message signal (VMS); dataset; machine learning; ADAS; PASCAL VOC; autonomous driving; deep learning; neural networks; retinanet (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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