Focusing on the issue that it is difficult to segment a specific object in a complex video scene, a video object segmentation method based on Dual Pyramid Network (DPN) was proposed. Firstly, the one-way transmission of modulating network was used to make the segmentation model adapt to the appearance of a specific object, which means, a modulator was learned based on visual and spatial information of target object to modulate the intermediate layers of segmentation network to make the network adapt to the appearance changes of specific object. Secondly, global context information was aggregated in the last layer of segmentation network by different-region-based context aggregation method. Finally, a left-to-right architecture with lateral connections was developed for building high-level semantic feature maps at all scales. The proposed video object segmentation method is a network which is able to be trained end-to-end. Extensive experimental results show that the proposed method achieves results which can be competitive to the results of the state-of-the-art methods using online fine-tuning on DAVIS2016 dataset, and outperforms other methods on DAVIS2017 dataset.