Abstract: A ship detection algorithm based on Mask region-convolutional neural network (Mask RCNN) was proposed to detect both the position and the ship flow in the satellite image. The proposed algorithm was designed to automatically detect and locate the vessel positions in the observed sea area. Based on the data set generated, the Mask RCNN model was built and trained. According to the position and accuracy of the outputs, the model parameters were modified to further improve the detection accuracy. Then, the trained model was applied to testing the image of the data set, quantitatively evaluating the model. The testing results show that, when the intersection over union (IOU) is 0.5, the accuracy of boundary frame position can reach 85.4% and the accuracy of ship number detection can reach up to 89.9%. The simulation results reveal that Mask RCNN can be used to detect the ship flow precisely.