Discrete Edge Feature Guided Rotation Detection Method for Remote Sensing Ship Wake

Ship wake rotation detection technology based on remote sensing images has a wide range of application value in ship supervision, trajectory prediction, and maritime rescue. However, objects in natural scenes usually present continuous and smooth features, while the contours and textures of ship wakes appear as discrete lines. This makes the wake have strong edge (discontinuity of image feature distribution) features but lack texture information, so the general detection model is difficult to perceive the wake completely, and low-dimensional edge features may also be ignored in deep networks. In addition, the significant directionality of wake also puts forward higher requirements for the accuracy of angle prediction. To address these issues, an efficient wake rotation detection algorithm is proposed in this study. First, we construct a discrete edge focused feature extraction module. This module features parallel edge branches that capture spatial features of the image while sensing changes in pixel gradient intensity, thereby obtaining a richer feature representation. Second, we construct an edge guided context attention fusion module. It uses the edge information in low-dimensional features as a guide to adaptively fuse contextual features, effectively enhancing the model’s global perception capability. Finally, we design a diagonal constrained probabilistic intersection over union. It further enhances the description of rotation angle and shape by considering the relative diagonal distance of minimum external horizontal rectangle, especially when dealing with square like objects. Experimental results show that our algorithm achieves accurate and stable ship wake detection, and has excellent robustness in weak objects and complex backgrounds. The mean average precision of this method for optical remote sensing wake detection reaches 95.1%, and the average error of angle prediction is reduced from 15.35$^{\circ }$ to 7.16$^{\circ }$. In addition, the algorithm also achieves an excellent performance in general remote sensing scenarios, which verifies its excellent adaptability and generalization.