Abstract: To solve the problem of performance degradation of existing segmentation model with low light condition, MDF-ANet based on RGB and Depth feature fusion network was proposed. First, the two-way feature extraction network was arranged to extract the RGB and depth features respectively, so as to perform sufficient feature learning on the original data. Then, a feature fusion module was designed to fuse the output feature maps of two-way feature extraction network at different scales. Finally, the fused output feature maps of each scale were input into the multi-scale upsampling fusion module to learn complementary information between different receptive fields. And then upsampling was performed to obtain the final segmented image with a size equal to the original input size. A series of experiments were carried out on Cityscapes and its converted faked-night dataset. Results show that 62.44% mean intersection over union (mIOU) is achieved on validating dataset. Compared with models trained with only RGB, the proposed model can obtain a 9.1% improvement, improving the image segmentation performance with low light condition effectively.