Abstract:SSD (Single Shot multi-box Detector) algorithm is used to detect multi-scale objects on feature maps of different layers, which has the characteristics of fast speed and high accuracy. However, the feature pyramid detection method of traditional SSD algorithm is difficult to fuse the features of different scales, and because the convolutional neural network layer at the bottom has weak semantic information and is not conducive to the recognition of small objects, so this paper proposes a novel object detection algorithm RF_SSD based on the network structure of SSD algorithm. In this algorithm, feature maps of different layers and scales are fused in a lightweight way, and new feature maps are generated in the lower sampling layer. By introducing the receptive field module, the feature extraction ability of the network is improved, and the characterization ability and robustness of the feature are enhanced. Compared with the traditional SSD algorithm, the accuracy of the proposed algorithm is significantly improved, and the real-time performance of object detection is fully guaranteed. The experimental results show that the accuracy is 80.2% and the detection speed is 44.5 FPS on the PASCAL VOC test set.