LAUSR

Vision-based Marine ship detection has been explored for many years, effectively improving maritime transportation management. However, many ship detection methods get troubles on the movable platform, which are summarized as follows: 1) high time efficiency is needed to handle the rapid changing of maritime scenes so that ships can be detected successfully on movable platform; 2) different appearances caused by sizes and viewpoints enlarge the intra-class distance of ships, which exceed the representation capability of some features like histogram of oriented gradients (HOG) with fixed size. For addressing these issues, we propose a rapid ship detection method based on multi-size gradient features and multi-branch support vector machine (SVM) in a “coarse-to-fine” manner that can be applied on a movable platform. The proposed multi-size gradient features are used to represent the ships with different sizes, including the coarse and fine gradient features. To speed up the detection process in a sliding way, the coarse ship locations are firstly generated only based on the coarse gradient features, which highly reduces the computational cost. Then, the multi-size gradient features extracted from these locations are determined by a multi-branch SVM model which is designed to deal with features of different dimensions and improve the precision of ship position with fine gradient features. The proposed method is tested on the changing background where ships have different sizes. Experimental results show that the proposed method obtains average precision (AP) of 0.795 and its detection speed achieves 60.6 frame per second, which achieve real-time performance and satisfactory detection precision.