Humanoid Robot Detecting Animals via Neural Network

Y. Yordanov, V. Mladenov
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Abstract

The recognition of objects via neural networks is gaining increasing popularity and usability in the world around us. For example - in the production lines of the factories where the details are recognized and then automatically sorted, in the fully automated stores where the camera systems and deep learning algorithms recognize the products we take from shelves and adds them to a virtual shopping cart, in banks where robots recognize people’s faces and offers them different services that the banks provide, or in the autonomous cars where it is needed quick and accurate recognition of the environment around the vehicles. This paper presents a neural network that can identify animals - with an existing set of pictures for training, it can recognize any animal. The pictures are taken from the robot’s camera. They’re then processed via a convolution neural network which is implemented via Tensorflow on a personal computer. As a result, the robot can identify and say the name of the animal standing in front of it.
基于神经网络的类人机器人检测动物
通过神经网络识别物体在我们周围的世界越来越受欢迎和可用性。例如,在工厂的生产线上,细节被识别然后自动分类,在全自动商店里,摄像头系统和深度学习算法识别我们从货架上取下的产品,并将它们添加到虚拟购物车中,在银行里,机器人识别人们的脸,并为他们提供银行提供的不同服务,或者在自动驾驶汽车中,需要快速准确地识别车辆周围的环境。本文提出了一种可以识别动物的神经网络——在已有的一组图片进行训练的情况下,它可以识别任何动物。这些照片是从机器人的相机上拍摄的。然后通过卷积神经网络进行处理,该网络通过个人电脑上的Tensorflow实现。因此,机器人可以识别并说出站在它面前的动物的名字。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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