iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications

Abstract

To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well.