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.

开发用于检测和跟踪道路车辆的自动化系统是智能交通系统（ITS）的一项艰巨的研究领域。本文提出了一种使用Faster R-CNN的多个区域建议网络（RPN）在变化的天气条件下进行道路车辆检测和跟踪的方法。在此研究领域中，尚未在Faster R-CNN中使用多个RPN。传统的Faster R-CNN通过单个固定大小的RPN生成感兴趣区域（ROI），因此无法检测大小不同的车辆，而本研究提出了一种在ROI较大的情况下进行端到端的道路车辆检测的方法。使用几个大小不一的RPN生成，因此它能够检测大小不一的车辆。所提出的方法的新颖之处在于在常规的Faster R-CNN中提出了几种不同大小的RPN。在变化的天气条件下检测到车辆。已使用三个不同的公共数据集，即DAWN，CDNet 2014和LISA数据集来评估所提出系统的性能，它分别提供了DAWN，CDNet 2014和LISA数据集的平均精度89.48％，91.20％和95.16％。 。在这方面，建议的系统优于现有方法。