Using the Improved Mask R-cnn and Softer-nms for Target Segmentation of Remote Sensing Image

Recently, the combination of remote sensing image processing and deep learning methods is an increasingly popular trend. In this paper, we combine the existing instance segmentation model Mask R-CNN and the target detection algorithm Softer-NMS, and propose a model based on the improved Mask R-CNN. The model can capture the edges well, thereby segmenting the target more accurately. The experimental results also verify the effectiveness of our method. Compared with the prototype Mask R-CNN, our method improves the overall segmentation accuracy, and the loss function of the mask branch and the loss function of the candidate frame of the RPN drop faster than the original model. Incidentally, there is also a good improvement in the accuracy of detection.