Group items tagged

* We examined vessel traffic threats on endangered killer whale foraging behavior. * Prey capture probability decreased as speed of proximate vessels increased. * Prey capture probability increased as estimates of preferred prey increased. * Vessel distance, noise and echosounders also influenced prey capture behavior. * Findings inform efforts to manage threats to endangered cetacean populations.

* Speed and distribution of small boats were investigated in Danish waters. * Boat speed increased with distance to marinas and varied among boat types. * Boats were most common close to the coast and near densely populated areas. * Harbour porpoises had a risk >10% of exposure to boats in 3% of the study area. * Boat noise may affect porpoises in coastal areas between Sweden and Denmark.

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.

The propagation of electromagnetic waves on land and sea is significantly different. Although the Los scenario is significant in marine wireless communication, the marine wireless channel exists an obvious two-ray phenomenon due to the strong reflection path reflected through the sea surface. By modeling the measured data of marine wireless channels, this paper calculates the radio propagation characteristics of the Pearl River estuary. In addition, the wave fluctuations and high humidity environment will also impact the properties of the marine wireless channel. Therefore, sea surface morphology models under multiple wind speeds are built. To estimate the path loss in the same area under different conditions, the Monte Carlo method is employed to quantify the results. The simulation results show that the electric wave propagation gradually degenerated from the round earth loss (REL) model to the free space model with increasing wind speed. Moreover, the distribution of the shadow fading varies with distance. The findings provide references for the network planning of marine communication

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

A new model based on daily oceanographic data and the movements of tagged whales has opened the potential for stakeholders to see where in the ocean endangered blue whales are most likely to be so that ships can avoid hitting them.