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Artificial neural networks have recently shown great results in manydisciplines and a variety of applications, including natural languageunderstanding, speech processing, games and image data generation. Oneparticular application in which the strong performance of artificial neuralnetworks was demonstrated is the recognition of objects in images, where deepconvolutional neural networks are commonly applied. In this survey, we give acomprehensive introduction to this topic (object recognition with deepconvolutional neural networks), with a strong focus on the evolution of networkarchitectures. Therefore, we aim to compress the most important concepts inthis field in a simple and non-technical manner to allow for future researchersto have a quick general understanding.  This work is structured as follows:  1. We will explain the basic ideas of (convolutional) neural networks anddeep learning and examine their usage for three object recognition tasks: imageclassification, object localization and object detection.  2. We give a review on the evolution of deep convolutional neural networks byproviding an extensive overview of the most important network architecturespresented in chronological order of their appearances.

A Non-Technical Survey on Deep Convolutional Neural Network Architectures