Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots

摘要

The paper describes an effort to train a convolutional neural network capable of reliably recognizing complex objects that are highly varied in their shapes and appearances in images. Neural networks show very good results on objects that have constant appearances but may have trouble recognizing abstract objects that appear in different shapes, art-styles and lack solid structure, for example, national flags. In an image, a flag may appear waving on a pole, as an element of clothes, in a form of stickers, etc. Due to these differences in appearance computer vision systems may show unsatisfactory results on these types of objects. However, detecting such objects is a necessary task in computer vision, especially for intelligent robots in order to understand the environment. The aim of the research is to apply convolutional neural networks for the detection of flags. In this research, we prepared training and testing sets of objects, trained a neural network for detection task, conducted testing experiments and measured the neural net’s performance. These results can be applied in cognitive and robotics technologies as well as general computer vision tasks.