Accurate Detection of Berthing Ship Target Based on Mask R-CNN

This paper mainly studies the berthing ship target detection method of overhead-view image under the condition of a few training samples. Because of the limited training samples, we use the complete data set unrelated to the target detection task for pre-training to obtain a classification model, then expand the data according to a certain percentage and finally complete the training of the target detection model. This paper uses the idea of segmentation to solve the target detection problem. We adjusted the configuration of the region proposal network including the size of anchor frame and the threshold of non-maximum suppression according to the target morphology, so that the network generates a more accurate region of interest. Finally, the confidence levels, bounding-boxes and image masks of multi-objective generated concurrently. We performed experiments on self-made data sets which labeled from NWPU VHR-10 and produced good results, which proved the feasibility of this method in target detection of berthing ship target.