Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

Zhao, Jiansen; Chen, Yanjun; Zhou, Zhenzhen; Zhao, Jingying; Wang, Shengzheng; Chen, Xinqiang

doi:10.1155/2022/3048611

Cited by 12 publications

(6 citation statements)

References 28 publications

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“…In busy waterways, AIS data can be incomplete or missing. To improve the accurate monitoring of ship speeds in such areas, Zhao et al used UAV (Unmanned Aerial Vehicle) onboard video for ship speed extraction [60]. They employed a simple linear tracking method with depth correlation metrics to extract ship speeds.…”

Section: Ship Navigation Behavior Traffic Flow Modeling and Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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Section: Ship Navigation Behavior Traffic Flow Modeling and Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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“…(2) Data processing and data fusion for maritime supervision: It is crucial to develop effective techniques for data processing and fusion to enhance the overall effectiveness of maritime drone surveillance [133]. The integration of vessel behaviour monitored by drones with vessel information obtained using the AIS can help detect potential violations and improve monitoring capabilities [134,135].…”

Section: Marine Safety and Surveillancementioning

confidence: 99%

Applications, Evolutions, and Challenges of Drones in Maritime Transport

Wang,

Zhou,

Xing

et al. 2023

JMSE

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The widespread interest in using drones in maritime transport has rapidly grown alongside the development of unmanned ships and drones. To stimulate growth and address the associated technical challenges, this paper systematically reviews the relevant research progress, classification, applications, technical challenges, and possible solutions related to the use of drones in the maritime sector. The findings provide an overview of the state of the art of the applications of drones in the maritime industry over the past 20 years and identify the existing problems and bottlenecks in this field. A new classification scheme is established based on their flight characteristics to aid in distinguishing drones’ applications in maritime transport. Further, this paper discusses the specific use cases and technical aspects of drones in maritime rescue, safety, navigation, environment, communication, and other aspects, providing in-depth guidance on the future development of different mainstream applications. Lastly, the challenges facing drones in these applications are identified, and the corresponding solutions are proposed to address them. This research offers pivotal insights and pertinent knowledge beneficial to various entities such as maritime regulatory bodies, shipping firms, academic institutions, and enterprises engaged in drone production. This paper makes new contributions in terms of the comprehensive analysis and discussion of the application of drones in maritime transport and the provision of guidance and support for promoting their further development and integration with intelligent transport.

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“…To obtain the pixel displacement of the ships in the images of continuous frames, the video with the detection frame information is taken as the input of the Deep SORT algorithm [8] in the third part of this framework. The algorithm first predicts the trajectory of the next frame using the Kalman filter.…”

Section: Ship Target Tracking With Deep Sort Algorithmmentioning

confidence: 99%

“…While several studies have been conducted on ship detection and tracking in maritime images [5][6][7], the related research has not sufficiently explored the application of ship detection and tracking technology, nor has it fully extracted the navigation information detection and tracking technology, nor has it fully extracted the navigation information from maritime images. Zhao et al [8] proposed a ship speed extraction framework based on UAV airborne video. In this study, the advantages of optical image data were fully utilized to realize the visual extraction of ship speed information.…”

Section: Introductionmentioning

confidence: 99%

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Zhou

Zhao

Chen

et al. 2023

JMSE

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Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation.

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Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

Cited by 12 publications

References 28 publications

A Review on Motion Prediction for Intelligent Ship Navigation

A Review on Motion Prediction for Intelligent Ship Navigation

Applications, Evolutions, and Challenges of Drones in Maritime Transport

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

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