In order to solve the problem of inferior recognition accuracy for small objects, a object detection algorithm guided by dual attention models is proposed. The method is based on the realization principle of single-stage detection algorithms, and introduces two attention models to improve the detection performance, especially for small objects. Specifically, a multi-scale feature cascade attention module is first introduced into the convolutional neural network, which weights the importance on different regions of the original convolutional neural network's feature map to reduce the interference of background and negative object information in the feature map, especially highlighting the small objects effectively in the shallow feature map. Besides, dense connection alleviates the problem of gradient disappearance in the process of back propagation. A salient channel self-attention module is introduced for the fused features, which focuses on the difference among different channels of the feature map so as to screen out useful channel information, thus making the feature map to be detected more representative. In addition, the experiments on COCO benchmark dataset of object detection verify the effectiveness and advancement of the proposed method.