Group items tagged

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under vari

With the development of the marine economy, video surveillance has become an important technical guarantee in the fields of marine engineering, marine public safety, marine supervision, and maritime traffic safety. In video surveillance, maritime object detection (MOD) is one of the most important core technologies. Affected by the size of maritime objects, distance, day and night weather, and changes in sea conditions, MOD faces challenges such as false detection, missed detection, slow detection speed, and low accuracy. However, the existing object detection algorithms usually adopt predefined anchor boxes to search and locate for objects of interest, making it difficult to adapt to maritime objects' complex features, including the varying scale and large aspect ratio difference. Therefore, this paper proposes a maritime object detection algorithm based on the improved convolutional neural network (CNN). Firstly, a differential-evolutionary-based K-means (DK-means) anchor box clustering algorithm is proposed to obtain adaptive anchor boxes to satisfy the maritime object characteristics. Secondly, an adaptive spatial feature fusion (ASFF) module is added in the neck network to enhance multi-scale feature fusion. Finally, focal loss and efficient intersection over union (IoU) loss are adopted to replace the original loss function to improve the network convergence speed. The experimental results on the Singapore maritime dataset show that our proposed algorithm improves the average precision by 7.1%, achieving 72.7%, with a detection speed of 113 frames per second, compared with You Only Look Once v5 small (YOLOv5s). Moreover, compared to other counterparts, it can achieve a better speed-accuracy balance, which is superior and feasible for the complex maritime environment.

In this paper, a novel fusion network for automatic modulation classification (AMC) is proposed in underwater acoustic communication, which consists of a Transformer and depth-wise convolution (DWC) network. Transformer breaks the limitation of sequential signal input and establishes the connection between different modulations in a parallel manner. Its attention mechanism can improve the modulation recognition ability by focusing on the key information. DWC is regularly inserted in the Transformer network to constitute a spatial-temporal structure, which can enhance the classification results at lower signal-to-noise ratios (SNRs). The proposed method can obtain more deep features of underwater acoustic signals. The experiment results achieve an average of 92.1% at −4 dB ≤ SNR ≤ 0 dB, which exceed other state-of-the-art neural networks.

It is significant for port ship scheduling and traffic management to be able to obtain more precise location and shape information from ship instance segmentation in SAR pictures. Instance segmentation is more challenging than object identification and semantic segmentation in high-resolution RS images. Predicting class labels and pixel-wise instance masks is the goal of this technique, which is used to locate instances in images. Despite this, there are now just a few methods available for instance segmentation in high-resolution RS data, where a remote-sensing image's complex background makes the task more difficult. This research proposes a unique method for YOLOv7 to improve HR-RS image segmentation one-stage detection. First, we redesigned the structure of the one-stage fast detection network to adapt to the task of ship target segmentation and effectively improve the efficiency of instance segmentation. Secondly, we improve the backbone network structure by adding two feature optimization modules, so that the network can learn more features and have stronger robustness. In addition, we further modify the network feature fusion structure, improve the module acceptance domain to increase the prediction ability of multi-scale targets, and effectively reduce the amount of model calculation. Finally, we carried out extensive validation experiments on the sample segmentation datasets HRSID and SSDD. The experimental comparisons and analyses on the HRSID and SSDD datasets show that our model enhances the predicted instance mask accuracy, enhancing the instance segmentation efficiency of HR-RS images, and encouraging further enhancements in the projected instance mask accuracy. The suggested model is a more precise and efficient segmentation in HR-RS imaging as compared to existing approaches.

Accurate and reliable dense mapping is crucial for understanding and utilizing the marine environment in applications such as ecological monitoring, archaeological exploration, and autonomous underwater navigation. However, the underwater environment is highly dynamic: fish and floating debris frequently appear in the field of view, causing traditional SLAM to be easily disturbed during localization and mapping. In addition, common depth sensors and depth estimation techniques based on deep learning tend to be impractical or significantly less accurate underwater, failing to meet the demands of dense reconstruction. This paper proposes a new underwater SLAM framework that combines neural radiance fields (NeRF) with a dynamic masking module to address these issues. Through a Marine Motion Fusion (MMF) strategy-leveraging YOLO to detect known marine organisms and integrating optical flow for pixel-level motion analysis-we effectively screen out all dynamic objects, thus maintaining stable camera pose estimation and pixel-level dense reconstruction even without relying on depth data. Further, to cope with severe light attenuation and the dynamic nature of underwater scenes, we introduce specialized loss functions, enabling the reconstruction of underwater environments with realistic appearance and geometric detail even under high turbidity conditions. Experimental results show that our method significantly reduces localization drift caused by moving entities, improves dense mapping accuracy, and achieves favorable runtime efficiency in multiple real underwater video datasets, demonstrating both its potential and advanced capabilities in dynamic underwater settings.

Synthetic aperture radar (SAR) images have all-weather observation capabilities and are crucial in ocean surveillance and maritime ship detection. However, their inherent low resolution, scattered noise, and complex background interference severely limit the accuracy of target detection. This paper proposes an innovative framework that integrates super-resolution reconstruction and multi-scale maritime ship detection to improve the accuracy of marine ship detection. Firstly, a TaylorGAN super-resolution network is designed, and the TaylorShift attention mechanism is introduced to enhance the generator's ability to restore the edge and texture details of the ship. The Taylor series approximation is combined to optimize the attention calculation, and a multi-scale discriminator module is designed to improve global consistency. Secondly, a hierarchical multi-scale Mask R-CNN (HMS-MRCNN) detection method is proposed, which significantly improves the multi-scale maritime ship detection problem through the cross-layer fusion of shallow features (small targets) and deep features (large targets). Experiments on SAR datasets show that TaylorGAN has achieved significant improvements in both peak signal-to-noise ratio and structural similarity indicators, outperforming the baseline model. After adding super-resolution reconstruction, the average precision and recall of HMS-MRCNN are also greatly improved.

The ships are believed to be fishing vessels originating in North Korea.

Three Chinese Container Terminals to Merge.

Introduction: Maritime transport is vital for global trade and cultural exchange, yet it carries inherent risks, particularly accidents at sea. Drones are increasingly valuable in marine search missions. However, Unmanned Aerial Vehicles (UAV) operating at high altitudes often leave only a small portion of a person overboard visible above the water, posing challenges for traditional detection algorithms.