Improvement of Automated Guided Vehicle's image recognition : Object detection and identification

Automated Guided Vehicle(AGV) as a kind of material conveying equipment has been widely used in modern manufacturing systems. [1] It carries the goods between the workshop along the designated paths. The ability of localization and recognizing the environment around themselves is the essential technology. AGV navigation is developed from several technologies such as fuzzy theory, neural network and other intelligent technology. Among them, visual navigation is one of the newer navigations, because of its path laying is easy to maintain, can identify variety of road signs. Compared with traditional methods, this approach has a better flexibility and robustness, since it can recognition more than one path branch with high anti-jamming capability. Recognizing the environment from imagery can enhance safety and dependability of an AGV, make it move intelligently and brings broader prospect for it. University West has a Patrolbot which is an AGV robot with basic functions. The task is to enhance the ability of vision analysis, to make it become more practical and flexible. The project is going to add object detection, object recognition and object localization functions on the Patrolbot. This thesis project develops methods based on image recognition, deep learning, machine vision, Convolution Neural Network and related technologies. In this project Patrolbot is a platform to show the result, we can also use this kind of program on any other machines. This report generally describes methods of navigation, image segmentation and object recognition. After analyzing the different methods of image recognition, it is easy to find that Neural Network has more advantages for image recognition, it can reduce the parameters and shorting the training and analyzing time, therefore Convolution Neural Network was introduced detailly. After that, the way to achieve image recognition using convolution neural network was presented and in order to recognize several objects at the same time, an image segmentation was also presented here. On the other hand, to make this image recognition processes to be used widely, the ability of transfer learning becomes important. Therefore, the method of transfer learning is presented to achieve customized requirement