Ship target detection based on multitask learning

Abstract

Ship target detection is of great significance in marine surveillance, rescue and so on. In this paper, in order to improve the performance of ship target detection, we proposed a ship target detection method based on multi task learning. There are mainly two contributions. Firstly, we designed a multi-task learning model by integrating segmentation module to the faster RCNN model. Through the strategies of feature sharing and joint learning, it is helpful to improve the accuracy of target detection with the assistance of segmentation; Secondly, in order to deal with the impact of initial anchor frame scale on target detection accuracy, we introduced an adaptive anchor width height ratio setting method based on improved K-means algorithm, by adaptively select initial anchor size suitable for the characteristics of ship targets, it is beneficial to further improve the detection accuracy. Moreover, we constructed an extended version of ship image data set including 14614 images belonging to 13 categories. Experimental results demonstrated that the proposed model can effectively improve the accuracy of ship target detection; and the comparison and the ablation experiments further validated the strategies of multi-task joint learning and adaptive anchor size setting is helpful for improving the performance of ship target detection.