Abstract: Coal mine complex scene monitoring images parsing is an important guarantee for safety and efficiency in coal mine operation.Semantic segmentation is a crucial way in the image intelligent analysis, which aims to assign a category label to each pixel in the image.High performance semantic segmentation models, such as Fully Convolutional Neural Networks, DeepLab, and DFN, depend on a large number of pixel level labels.There are some problems in the task of coal mine monitoring image semantic segmentation, such as the lack of monitoring image annotation information and the confusion of different semantic targets with similar appearances.Therefore, the Dual Alignment Networks method is proposed.The method reduces the domain difference in the feature level and pixel level, and can transfer the image semantic segmentation model trained on the synthetic data to the coal mine scene for monitoring images semantic segmentation.In the feature space, the feature level domain adaptation network is used to learn the domain invariant features, which can reduce the feature representation distribution difference between the two domains.In the pixel space, the pixel level domain adaptation network is used to transfer the source images to the style of target images, which can reduce the domain shift caused by texture and illumination.To enhance the discrimination of different categories of targets in the coal mine monitoring image, the stylized images are used to train the segmentation network, making it can learn the characteristics of coal mine monitoring image illumination and texture.To improve the discriminative ability of the discriminator, the spatial attention module and channel attention module are involved in discriminator.The channel attention module assigns different weights to each channel features, and the spatial attention module obtains the relationship information between different positions by non local operation.To evaluate the effectiveness of Dual Alignment Networks, the algorithm is compared with AdaptSegNet, DCAN,and CLAN in GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes domain adaptation tasks.The experimental results show that the Mean Intersection over Union (MIoU) of the Dual Alignment Networks is 43.7% and 45.80%.For a coal mines complex scene, the algorithm is compared with AdaptSegNet, DCAN, and CLAN in SYNTHIA-to-Coal Mine domain adaptation task.The Mean Intersection over Union of Dual Alignment Networks is 38.26%, which is increased by 7.19%, 8.34% and 5.56% respectively.For some coal mine monitoring images without annotations, the Dual Alignment Networks can segment different semantic categories targets by reducing the difference between the synthetic image and the coal mine monitoring image.