With the advancement of deep learning, the application of convolutional neural network(CNN) in the field of target detection has made significant improvements. Compared with the traditional manual feature construction methods, the CNN algorithm has automatic feature extraction and strong generalization ability, which makes the target detection task better solved in complex scenarios. In addition, the algorithm has better robustness, making it more reliable and efficient in practical applications. Firstly, the development history of CNN was reviewed, focusing on its application in the field of computer vision and highlighting the obvious advantages in image processing compared with traditional algorithms. Then, three mainstream algorithms, namely, classification and segmentation algorithms, target detection algorithms, and region and regression based detection, were introduced, with special emphasis on the innovations and breakthroughs of each type of algorithm in solving the key problems of target localization, classification, and semantic segmentation. Then, the existing public datasets were summarized. Finally, the future development trend of target detection algorithms was envisioned.