An Enhanced SiamMask Network for Coastal Ship Tracking

Abstract

Coastal ship tracking is significant for cargo transportation and route determination. However, there are few effective tracking methods for specialized ship tracking. Although Siamese networks have been commonly used for object tracking in the field of deep learning, the results for the ship are not accurate due to the lack of contour and edge information. In addition, scale variation and seawater cause unstable ship movements, which aggravates the reduction in tracking accuracy. Therefore, we propose an enhanced SiamMask network for coastal ship tracking. Compared to the previous Siamese network, our algorithm has the following three advantages. First, we apply the unity of visual object tracking and semisupervised object segmentation to the ship tracking task, which completes target tracking while outputting edge shape information. Second, we propose a refined feature pyramid network that utilizes a feature fusion module and enhanced residual module (ERM) to solve the problems of scale variation in datasets. Third, we propose an attentionwise cross correlation with a multidimension attention module (MDAM) to focus more on ship targets and suppress nontargets through autonomous learning at width, height, and channel levels, which creates a tradeoff between the accuracy and the robustness of tracking algorithms. The experimental results show that our method achieves leading performance in LMD-TShips, outperforming most of the state-of-the-art trackers. Code is available at <uri>https://gitee.com/EnhancedSiamShipTracking/code</uri>.