A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking
In the context of vessel monitoring, the accuracy of vessel speed measurements is contingent on the availability of AIS data. However, the absence, failure, or signal congestion of AIS devices may lead to delays and inaccuracies in the speed information. To address this challenge, this paper propose...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3884 |
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| author | Zhe Ma Qinyou Hu Yuezhao Wu Wei Wang |
| author_facet | Zhe Ma Qinyou Hu Yuezhao Wu Wei Wang |
| author_sort | Zhe Ma |
| collection | DOAJ |
| description | In the context of vessel monitoring, the accuracy of vessel speed measurements is contingent on the availability of AIS data. However, the absence, failure, or signal congestion of AIS devices may lead to delays and inaccuracies in the speed information. To address this challenge, this paper proposes a vessel speed detection method based on target detection and tracking to acquire vessel speed in real time. The proposed methodology involves the establishment of a mapping relationship between image coordinates and four real-world coordinates, ensuring precise conversion from pixel velocity to physical velocity. Subsequently, a frame difference method combined with a multi-frame averaging strategy calculates the vessel speed. Furthermore, an advanced M-YOLOv11 detection model is introduced to enhance the detection performance in different vessel shapes and complex environments, thus ensuring the accuracy of speed information is further improved. The experimental results demonstrate that M-YOLOv11 exhibits a significant performance enhancement, with a 13.95% improvement in the average precision metric over the baseline model. Over 60% of the measured vessel speed measurement errors are less than 0.5 knots, with an overall average error below 0.45 knots. These findings substantiate the efficacy and superiority of the proposed method in practical applications. |
| format | Article |
| id | doaj-art-c60b57d2a7cb4bd18b3284909d5cedd0 |
| institution | Matheson Library |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-c60b57d2a7cb4bd18b3284909d5cedd02025-07-11T14:42:50ZengMDPI AGSensors1424-82202025-06-012513388410.3390/s25133884A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual TrackingZhe Ma0Qinyou Hu1Yuezhao Wu2Wei Wang3College of Merchant Marine, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Merchant Marine, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Merchant Marine, Shanghai Maritime University, Shanghai 201306, ChinaJiujiang Maritime Bureau, Jiujiang 332001, ChinaIn the context of vessel monitoring, the accuracy of vessel speed measurements is contingent on the availability of AIS data. However, the absence, failure, or signal congestion of AIS devices may lead to delays and inaccuracies in the speed information. To address this challenge, this paper proposes a vessel speed detection method based on target detection and tracking to acquire vessel speed in real time. The proposed methodology involves the establishment of a mapping relationship between image coordinates and four real-world coordinates, ensuring precise conversion from pixel velocity to physical velocity. Subsequently, a frame difference method combined with a multi-frame averaging strategy calculates the vessel speed. Furthermore, an advanced M-YOLOv11 detection model is introduced to enhance the detection performance in different vessel shapes and complex environments, thus ensuring the accuracy of speed information is further improved. The experimental results demonstrate that M-YOLOv11 exhibits a significant performance enhancement, with a 13.95% improvement in the average precision metric over the baseline model. Over 60% of the measured vessel speed measurement errors are less than 0.5 knots, with an overall average error below 0.45 knots. These findings substantiate the efficacy and superiority of the proposed method in practical applications.https://www.mdpi.com/1424-8220/25/13/3884vessel speedtarget detectionvisual trackingmachine visualization |
| spellingShingle | Zhe Ma Qinyou Hu Yuezhao Wu Wei Wang A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking Sensors vessel speed target detection visual tracking machine visualization |
| title | A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking |
| title_full | A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking |
| title_fullStr | A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking |
| title_full_unstemmed | A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking |
| title_short | A Method for Real-Time Vessel Speed Measurement Based on M-YOLOv11 and Visual Tracking |
| title_sort | method for real time vessel speed measurement based on m yolov11 and visual tracking |
| topic | vessel speed target detection visual tracking machine visualization |
| url | https://www.mdpi.com/1424-8220/25/13/3884 |
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