International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 18 (2018) pp. 13945-13949 © Research India Publications. http://www.ripublication.com 13945 Object Recognition in Dynamic Environments with Convolutional Neural Networks Robinson Jiménez-Moreno 1 , Oscar Avilés Sánchez 2 , Diana Marcela Ovalle 3 1 Professor, Department of Mechatronics Engineering, Nueva Granada Military University, Bogotá, Colombia. 2 Professor, Department of Mechatronics Engineering, Nueva Granada Military University, Bogotá, Colombia. 3 Professor, Department of Electronics Engineering, Distrital Frco. Jose de Caldas University, Bogotá, Colombia. Abstract Deep learning techniques have revolutionized pattern recognition systems over the last decade. Within these techniques, convolutional neural networks (CNN) have demonstrated a high performance in the recognition of objects in images. This article presents the evaluation of a CNN with 93% accuracy in the recognition of objects of four categories, trained with images at a fixed distance and the variations of said performance when evaluating said network with images of the trained categories, with variations of distance, evidencing a degradation of said performance by 30%. This makes it possible to establish the need for a structured convolutional neural architecture that, in function of the advantages of training of fixed distance by parallel layers, reinforces learning to accuracy values higher than 90%, whose incidence is demarcated in robotic mobile applications where the camera approaches or distances from the object. Keywords. Machine vision, convolutional neural network, object recognition, MATLAB, Image RGB-D. INTRODUCTION Machine vision algorithms were usually developed in three basic stages, the one of pre-processing of image for adequacy of the same in size, elimination of noise and others, stage of image processing oriented to extraction of characteristics and a stage of recognition of patterns or classification, the latter usually developed through neural networks. With the development of different deep learning techniques [1], and within these, of convolutional neural networks (CNN) [2], pattern recognition systems have become more versatile. A clear example of this, CNNs are now widely used in pattern recognition [3] and image identification applications [4], with very high accuracy ranges above 90% in most cases, and with the ability to integrate the traditional steps described at the beginning. Although they are still developing techniques [5], CNN offer solutions in different areas [6], such as artificial intelligence, robotics, medicine, intelligent control systems and the development of intelligent city schemes such as traffic control applications, etc. So that, as they are applied in different environments, opportunities for developing these techniques are found, as a function of the capabilities they offer. In the field of robotics, several developments have been presented that allow the interaction of a robot with its medium, for it is necessary to capture and interpret it, which is achieved by image capture sensors such as cameras and RGB-D sensors, i.e., that apart from the image, they give the depth to which the objects in the captured scene are. CCNs have already been involved in such applications [7] [8] at the level of conventional RGB and depth [9-11]. However, mobile robotics applications are still under development and, as mentioned in [12] [13], the combination of information of RGB-D and CNN presents an important nucleus for the development of applications of this type. When a robot moves, the distance from the camera to the object varies, which presents a dynamic learning characteristic of the object. It is clear that an object presents different perspectives depending on the distance in which it is observed, as the focus of vision moves away from an object, specific features of the object are lost and the outline is the most relevant information, as it approaches the object specific details appear. This can present a challenge in the performance of a CNN when identifying an object, when it is used from a mobile robot that approaches or distances an object of interest. In the state of the art this analysis is not found, for which it is addressed in the present article. This article is organized as follows. In section 2 the CNN architecture to be used is presented, the generalities of the training of this type of networks and the results of the training for the recognition of objects at a fixed distance. In section 3 it is presented the analysis of the trained network with distance variations in the test database and a new training including this new database. In section 4 the conclusions of the evaluation of the network in dynamic atmospheres of image capture input for prediction of CNN are presented. Convolutional neuronal network architecture Robotic agents handle different path planning schemes to achieve a specific goal. For this task it is fundamental to recognize the environment in which it moves and the objective that seeks, typically this objective is an object, as is the case of