Comparison of faster R-CNN models for object detection

Abstract

Object detection is one of the important problems for autonomous robots. Faster R-CNN, one of the state-of-the-art object detection methods, approaches real time application; nevertheless, computational time lies borderline of real time application, i.e. 5fps with VGG16 model in K40 GPU system in [1]. Moreover, computation time depends on model and image crop size, but precision is also affected; usually, time and precision have trade-off relation. By adjusting input image size in spite of downgrading performance, computation time meets criteria for one model. Therefore, selection of a model is one of the important problems when faster R-CNN based object detection system for an autonomous robot is constructed. In this paper, we convert several state-of-the-art models from convolution neural network (CNN) for image classification. Then, we compare converted models with several image crop size in terms of computation time and detection precision. We will utilize those comparison data for selecting a proper detection model in case a robot needs to perform an object detection task.