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 computati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/108 |
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| author | Chengcheng Yu Yanmei Zhang |
| author_facet | Chengcheng Yu Yanmei Zhang |
| author_sort | Chengcheng Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ac5c589ce163442bb262c28eab905f48 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ac5c589ce163442bb262c28eab905f482025-01-24T13:36:52ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113110810.3390/jmse13010108TriangularSORT: A Deep Learning Approach for Ship Wake Detection and TrackingChengcheng Yu0Yanmei Zhang1School of Integrated Circuits and Electronic Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Integrated Circuits and Electronic Engineering, Beijing Institute of Technology, Beijing 100081, ChinaShip 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.https://www.mdpi.com/2077-1312/13/1/108ship trackingwake detectiondeep learningtriangular IoU |
| spellingShingle | Chengcheng Yu Yanmei Zhang TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking Journal of Marine Science and Engineering ship tracking wake detection deep learning triangular IoU |
| title | TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking |
| title_full | TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking |
| title_fullStr | TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking |
| title_full_unstemmed | TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking |
| title_short | TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking |
| title_sort | triangularsort a deep learning approach for ship wake detection and tracking |
| topic | ship tracking wake detection deep learning triangular IoU |
| url | https://www.mdpi.com/2077-1312/13/1/108 |
| work_keys_str_mv | AT chengchengyu triangularsortadeeplearningapproachforshipwakedetectionandtracking AT yanmeizhang triangularsortadeeplearningapproachforshipwakedetectionandtracking |