Revisiting Deep Learning Models for Road Lane Detection

This paper revisits the prominent deep-learning based lane-detection (DLLD) models and their datasets. Deep learning has redefined the limits of the recent computer-vision based models for the emerging intelligent transportation system with assisted/self-driving. However, these models are not readily applicable to the irregular and complex lanes in the developing and underdeveloped countries. In support of this development and validation of the DLLD models, a balanced mix of varying roads and scene-conditions, including the rural, suburban and urban areas of the developing and underdeveloped countries, different traffic, weather and lighting conditions, moving artefact (i.e., motion-blurring), and irregular and unmarked lanes are determined. In light of these conditions, we then analyse the merits and limitations of promising DLLD models and available datasets. Finally, the avenues and need for the future development of those models and datasets are suggested to ensure their real-time and universal applicability to the unstructured and complex road-lane scenarios, for the safer roads and better traffic.