A review of object detection based on convolutional neural network

With the development of intelligent device and social media, the data bulk on Internet has grown with high speed. As an important aspect of image processing, object detection has become one of the international popular research fields. In recent years, the powerful ability with feature learning and transfer learning of Convolutional Neural Network (CNN) has received growing interest within the computer vision community, thus making a series of important breakthroughs in object detection. So it is a significant survey that how to apply CNN to object detection for better performance. First the paper introduced the basic concept and architecture of CNN. Secondly the methods that how to solve the existing problems of conventional object detection are surveyed, mainly analyzing the detection algorithm based on region proposal and based on regression. Thirdly it mentioned some means which improve the performance of object detection. Then the paper introduced some public datasets of object detection and the concept of evaluation criterion. Finally, it combed the current research achievements and thoughts of object detection, summarizing the important progress and discussing the future directions.