Research on Improving the Reliability of Vision Positioning System by Deep Learning

Vision positioning system is widely used in robotics, industrial testing, servo control and other autonomous systems. Precise object positioning needs to accurately match the feature key points of the object, so as to solve the object position and attitude precisely. Simple image or feature matching method cannot meet the requirements of both reliability and accuracy for positioning system. Recently, deep learning has aroused widespread concern, Caffe as a framework for deep learning, performs particularly well in image recognition. In this paper, we propose a method of using convolutional neural networks to recognize target in complex scenes, and then to obtain the information of the target position by precise image matching. The experimental results show that the proposed method effectively eliminates the false target recognition and improves the image matching accuracy. Introduction In recent years, due to the rapid development of image processing and computer vision, machine vision has been widely used in aerospace, manufacturing, industrial, medical and electronics fields. The most important function of machine vision is to identify and locate the target. The traditional method to localize the target from the image is to directly adopt the precise image matching, but this method does not work well to distinguish the target in a complex background and often leads to a lot of false matching and positioning accuracy decline. With the introduction of high precision matching requirements, researchers begin to study how to obtain high-precision, more reliable image matching results for complex scenes. Keller [1] proposed a high precision rotation angle estimation method based on angular difference function and demonstrated its applicability to rotation estimation; Yoshihiko Mochizuki [2] of Chiba University of Japan proposed a high precision optical flow estimation method to recover a high-resolution image from a low-resolution image and/or image sequence; and the Qingxiong Yang [3] of the University of Illinois proposed a high precision global stereo matching method based on image segmentation. In addition, J Molnár [4] et al. also achieved many results in matching complex scene images and improving the reliability of matching algorithms. He proposed two illumination-robust variational methods based on cross-correlation that are applicable to color and grey-level sequences and robust to brightness and color illumination changes. However, in the complex environment of low brightness, low image resolution and target occlusion, the traditional visual positioning cannot achieve accurate recognition. With the development of machine learning, the deep learning shows higher robustness in some complex environments, and is more conducive to solve the problem of target recognition in complex scenes. This paper presents a method of target recognition based on convolution neural network. We adopt the CNN method to firstly recognize the target whether there is a target in the scene or not, and then perform accurate image matching and get the coordinate of the target image for precise position