<![CDATA[The Convolutional neural network (CNN) has significantly exposed a great performances and growing desire in the field of image processing within the research community, through relevant innovations in object detection by magnificent capacity in transfer learning and feature learning. With the advancement of CNN in object detection, huge amount of data is process with great speed. In respect to CNN, object detection has greatly advanced and become popular in the research community, security experts, traffic experts, and remote sensing community etc. In this review, comprehensive study of various CNN architecture for object detection in images based on conventional approached, novelty, and achievement were analysed in details. Therefore, it is an important review on how to achieve high performance in object detection via CNN. We first introduced the basic idea on CNN models and their improvement in detecting object. Secondly, we review CNN and its variant such as, ResNet, VGG, GoogleNet and other CNN architectures. Thirdly, we mention some performance metrics used for object detection. Lastly, we analyse some main contribution of CNN algorithm with their remarkable achievement and further analyse the challenge and its future direction]]>
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