Abstract: With the development of hardware resources, deep learning technology is widely used in computer vision tasks. Object detection methods based on deep learning have become the mainstream. Feature fusion is a common way to improve model performance in object detection, and there is no systematic summary work yet. Therefore, with the background of deep learning, this paper firstly introduces feature fusion from the local and overall levels, and analyzes the typical and improved feature fusion methods in object detection according to the fusion form of layer, flow and space in structure. Then the commonly used feature fusion techniques in the backbone network and neck network are analyzed, such as deepening the network, expanding the receptive field, and weighted fusion. Finally, the future research direction of feature fusion is proposed, and the development prospects of multimodal fusion, adaptive fusion and attention mechanism are analyzed, which provides useful guidance for follow-up work.