Multiship Speed Measurement Method Based on Machine Vision and Drone Images

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

doi:10.1109/tim.2023.3272406

Cited by 7 publications

(4 citation statements)

References 25 publications

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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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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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“…Lu et al [39] improved the YOLOv5 algorithm for improved adaptation to UAV applications in maritime and fishery enforcement. Zhao et al [40] combined the YOLOv4 algorithm with a deepsort for multi-boat speed measurements using UAV images. Bai et al [41] deployed an enhanced YOLOv5s algorithm in UAVs to search for individuals who have fallen overboard.…”

Section: Object Detection Based On Uav Imagesmentioning

confidence: 99%

An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images

Zhang,

Yin,

Shao

2023

Remote Sensing

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Unmanned aerial vehicles (UAVs), renowned for their rapid deployment, extensive data collection, and high spatial resolution, are crucial in locating distressed individuals during search and rescue (SAR) operations. Challenges in maritime search and rescue include missed detections due to issues including sunlight reflection. In this study, we proposed an enhanced ABT-YOLOv7 algorithm for underwater person detection. This algorithm integrates an asymptotic feature pyramid network (AFPN) to preserve the target feature information. The BiFormer module enhances the model’s perception of small-scale targets, whereas the task-specific context decoupling (TSCODE) mechanism effectively resolves conflicts between localization and classification. Using quantitative experiments on a curated dataset, our model outperformed methods such as YOLOv3, YOLOv4, YOLOv5, YOLOv8, Faster R-CNN, Cascade R-CNN, and FCOS. Compared with YOLOv7, our approach enhances the mean average precision (mAP) from 87.1% to 91.6%. Therefore, our approach reduces the sensitivity of the detection model to low-lighting conditions and sunlight reflection, thus demonstrating enhanced robustness. These innovations have driven advancements in UAV technology within the maritime search and rescue domains.

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“…Ship emissions (SEs) pollution may degrade air quality, exacerbating the global warming problem and harming human health [1]. Approximately 3% of global greenhouse gas emission is derived from the shipping industry, and this figure may increase in the future with global trade development [2,3]. The global greenhouse gas (GHG) emissions from the shipping industry rose 9.6% from 977 million tons in 2012 to 1076 million tons in 2018.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Chen,

Dou,

Song

et al. 2024

JMSE

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Ship pollution emissions have attracted increasing attention in the maritime field due to the massive growth of maritime traffic activities. It is important to identify the ship emissions (SEs) magnitude and corresponding spatial and temporal distributions for the purposes of developing appropriate strategies to mitigate environment pollution. The aim of this study was to estimate ship pollution emissions with various typical merchant ship types under different sailing conditions. We estimated the emission variation with a ship traffic emission assessment model (STEAM2), and then the ship pollution emission distribution was further visualized using ArcGIS. We collected data from the automatic identification system (AIS) for ships in New York Harbor and further analyzed the spatiotemporal distribution of pollutant emissions from ships. The experimental results demonstrate that the ship pollutant emission volume in the New York Harbor area in 2022 was 3340 t, while the pollution in terms of CO, SO2, CXHX, PM10, NOX, and PM2.5 was 136, 1421, 66, 185, 1384, and 148 t, respectively. The overall SEs from container ships, passenger ships, and tankers account for a large amount of pollution discharge. The pollutant emissions of container ships are significantly greater than that of their counterparts. Moreover, the spatiotemporal distributions of ship pollutant discharge can vary significantly among different ship types and sailing conditions.

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Multiship Speed Measurement Method Based on Machine Vision and Drone Images

Cited by 7 publications

References 25 publications

Applications, Evolutions, and Challenges of Drones in Maritime Transport

Applications, Evolutions, and Challenges of Drones in Maritime Transport

An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

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