Deep learning has recently achieved breakthrough progress in speech recognition and image recognition. With the advent of big data era, deep convolutional neural networks with more hidden layers and more complexarchitectures have more powerful ability of feature learning and feature representation. Convolutional neural network models trained by deep learning algorithm have attained remarkable performance in many large scale recognition tasks of computer vision since they are presented. In this paper, the arising and development of deep learning and convolutional neural network are briefly introduced, with emphasis on the basic structure of convolutional neural network as well as feature extraction using convolution and pooling operations. The current research status and trend of convolutional neural networks based on deep learning and their applications in computer vision are reviewed, such as image classification, object detection, pose estimation, image segmentation and face detection etc. Some related works are introduced from the following three aspects, i.e., construction of typical network structures, training methods and performance. Finally, some existing problems in the present research are briefly summarized and discussed and some possible new directions for future development are prospected.