Image dataset for Persian Road Surface Markings
Seyed Hamid Safavi, Mohammad Eslami, Aliasghar Sharifi, Amirhosein Hajihoseini, Mohammadreza Riahi, Maryam Rekabi, Sadaf Sarrafan, Rahman Zarnoosheh, Ehsan Khodapanah Aghdam, Sahar Barzegari Banadkoki, Seyed Mohammad Seyedin Navadeh, Farah Torkamani-Azar
Abstract:
Self-driving and autonomous cars are hot emerging technologies which can provide enormous impact in the near future. Since an important component of autonomous cars is vision processing, the increasing interest for self-driving cars has motivated researchers to collect different relative image datasets. Hence, we collect a comprehensive dataset about the road surface markings which are available in Iran. In addition, we evaluate the conventional recognition rate. In this paper, we present a novel and extensive dataset for Persian Road Surface Markings (PRSM) with ground truth labels. We also hope that it will be useful as a Persian benchmark dataset for researchers in this field. The dataset consists of over 68,000 labeled images of road markings in 18 popular classes. It also contains road surface markings under various daylight conditions. Our dataset with
further details is available online at: http://display.sbu.ac.ir/databases.
