On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks

Abstract

Developing automated systems to detect and track on-road vehicles is a demanding research area in Intelligent Transportation System (ITS). This article proposes a method for on-road vehicle detection and tracking in varying weather conditions using several region proposal networks (RPNs) of Faster R-CNN. The use of several RPNs in Faster R-CNN is still unexplored in this area of research. The conventional Faster R-CNN produces regions-of-interest (ROIs) through a single fixed sized RPN and therefore cannot detect varying sized vehicles, whereas the present investigation proposes an end-to-end method of on-road vehicle detection where ROIs are generated using several varying sized RPNs and therefore it is able to detect varying sized vehicles. The novelty of the proposed method lies in proposing several varying sized RPNs in conventional Faster R-CNN. The vehicles have been detected in varying weather conditions. Three different public datasets, namely DAWN, CDNet 2014, and LISA datasets have been used to evaluate the performance of the proposed system and it has provided 89.48%, 91.20%, and 95.16% average precision on DAWN, CDNet 2014, and LISA datasets respectively. The proposed system outperforms the existing methods in this regard.