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Curb detection is an essential function of autonomous vehicles in urban areas. However, curbs are difficult to detect in complex urban environments in which many dynamic objects exist. Additionally, curbs appear in a variety of shapes and sizes. Previous studies have been based on the traditional pipeline, which consists of the extraction and aggregation of hand-crafted features that are then fed to classifiers. However, this sequential process is inefficient and designing the hand-crafted features is a complex process. Recently, this kind of process has been replaced by Deep Neural Networks (DNN), in which classifiers and features are learned from large-scale data. Very few works have exploited DNN for the curb detection problem. Most works use multi-modal sensor-based methods that combine images and accumulated 3D point clouds from LIDAR. However, these approaches require synchronization and …