Joint image enhancement and localization framework for vehicle model recognition in the presence of non-uniform lighting conditions

Abstract

Recognizing the model of a vehicle in natural scene images is an important and challenging task for real-life applications. Current methods perform well under controlled conditions, such as frontal and horizontal view-angles or under optimal lighting conditions. Nevertheless, their performance decreases significantly in an unconstrained environment, that may include extreme darkness or over illuminated conditions. Other challenges to recognition systems include input images displaying very low visual quality or considerably low exposure levels. This paper strives to improve vehicle model recognition accuracy in dark scenes by using a deep neural network model. To boost the recognition performance of vehicle models, the approach performs joint enhancement and localization of vehicles for non-uniform-lighting conditions. Experimental results on several public datasets demonstrate the generality and robustness of our framework. It improves vehicle detection rate under poor lighting conditions, localizes objects of interest, and yields better vehicle model recognition accuracy on low-quality input image data.