TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking

Ship wake detection and tracking are of paramount importance for ensuring maritime safety, conducting effective ocean monitoring, and managing maritime affairs, among other critical applications. This paper introduces a novel approach for ship tracking and wake detection utilizing advanced computational techniques, particularly the TriangularSORT algorithm for monitoring vessels. This method enhances effective ship tracking by closely associating the vertices of the triangular wake with the coordinates of the ship. Furthermore, this paper integrates the triangular IoU and attention mechanism, introducing the Triangular Attention Mechanism. This mechanism guides the model's focus to key areas of the image by defining triangular points on the feature map, thereby enhancing the model's ability to recognize and analyze local features in visual tasks. Experimental results demonstrate that the proposed method significantly improves the performance and accuracy of models in object detection and tracking tasks.