With the exploration of the excellent feature extraction capabilities of deep convolutional neural networks, target detection has made a great stride. At the same time, the target detection technology combined with deep learning has achieved remarkable results. It has been widely used in such real scenarios as automatic driving, intelligent transportation systems, drone scenarios, military target detection, and medical navigation. The study reviews the shortcomings of traditional target detection algorithms and introduces commonly used detection data sets and performance evaluation indicators. It also summarizes classic target detection algorithms based on deep learning and elaborates on current target detection and existing difficulties and challenges. The feasible research directions in the future are prospected.