Convolutional Neural Networks are the state of the art for computer vision problems such as classification and detection. Networks like YOLO and SSD have demonstrated excellent results on benchmark datasets such as the PASCAL VOC and COCO datasets. However these networks only run at real time with the support of powerful GPUs and are infeasible for use in low power embedded real-time robotic applications. Pruning has been shown to be an efficient technique for reducing the runtime computational cost of a neural network while maintaining performance in image classification tasks. In this work we evaluate the efficacy of pruning on the problem of object detection using a modified tiny-YOLO network. The network was trained on a custom object detection task and three pruning techniques were evaluated, including our contribution which specifically targets reducing the FLOPS in the network. The results show that pruning with our method followed by extended fine-tuning achieved a 4.5x reduction in FLOPS and a 7x reduction in parameters with no drop in accuracy.