A Deformable Spatial Attention Mechanism-Based Method and a Benchmark for Dock Detection

Abstract

Dock is a significant site in the shipbuilding industry. The detection of docks contributes to many important fields. With the abundant methods and datasets, the deep learning-based object detection in remote sensing images has received wide attention. However, there is no dataset that includes the dock class. This paper firstly proposes a dock dataset to build a benchmark and advance dock detection research. Further, object detection of docks using existing methods cannot yield convincing results due to the characteristics of docks. To meet the challenges in dock detection, a novel deformable spatial attention module (DSAM) is proposed to enhance the feature representation and localization of docks. Based on the DSAM, a novel network architecture is proposed to perform accurate and efficient dock detection. The ablation and comparison experiments reveal that the proposed methods are accurate and effective, which are superior to the existing methods. Dock dataset can be downloaded in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://drive.google.com/drive/folders/1_ao1tOQtmO_aanoHXGPSkuqRRqWJaCjk?usp</uri> = share_link.