Image Processing Approaches for Autonomous Navigation of Terrestrial Vehicles in Low Illumination

Computer vision can be used as an integral part of any autonomous systems. Visual input and processing enables faster and early decisions. An important challenge in computer vision is detection and recognition of objects. This challenge is more pronounced in low illumination. In this paper, we are proposing a detection and recognition model for road warning signs with voice notification system for both autonomous and usual vehicles considering varied level of illumination. Realtime video from the vehicles was analysed using opencv. The noise from the video was removed using filters. Detection was based on Haar-cascades and training was done with sample positive and negative images. Text recognition was based on pattern matching. Voice notification was done using string to voice converters. The night vision was lightened considering the glare of vehicles headlight.