Object detection can be difficult due to challenges such as variations in objects both inter- and intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object detectors. Ensemble methods explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with lowered variance with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination strategies were explored for combining the individual member detections into an ensemble result. R-FCNs were trained and tested on the PASCAL VOC benchmark object detection dataset. Results proved positive with an increase in AP when ensemble members were combined appropriately. The method shows potential and other object or image variations could be sampled to see if a more robust ensemble could be made.