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Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.

Anthropogenic noise is a recent addition to the list of human-made threats to the environment, with potential and established negative impacts on a wide range of animals. Despite their economic and ecological significance, few studies have considered the impact of anthropogenic noise on crustaceans, though past studies have shown that it can cause significant effects to crustacean physiology, anatomy, and behaviour. Mating behaviour in crustaceans could potentially be severely affected by anthropogenic noise, given that noise has been demonstrated to impact some crustacean's ability to detect and respond to chemical, visual, and acoustic cues, all of which are vital in courtship rituals. To explore if noise has an impact on crustacean mating, we tested the responses of male green shore crabs (Carcinus maenas) from the southwest UK coast by exposing them to ship noise recordings while simultaneously presenting them with a dummy-female soaked in the female-sex pheromone uridine diphosphate (UDP) in an experimental tank setup (recording treatment: n = 15, control treatment: n = 15). We found a significant, negative effect of noise on the occurrence of mating behaviour compared to no noise conditions, though no significant effect of noise on the time it took for a crab to respond to the pheromone. Such effects suggest reproductive impairment due to anthropogenic noise, which could potentially contribute to decreased crustacean populations and subsequent ecological and economic repercussions. Given the findings of our preliminary study, more research should be undertaken that includes larger sample sizes, double blind setups, and controlled laboratory trials in order to more fully extrapolate the potential impact of noise on mating in the natural environment.

Adverse weather conditions such as rain and haze often lead to a degradation in the quality of maritime images, which is crucial for activities like navigation, fishing, and search and rescue. Therefore, it is of great interest to develop an effective algorithm to recover high-quality maritime images under adverse weather conditions. This paper proposes a prompt-based learning method with degradation perception for maritime image restoration, which contains two key components: a restoration module and a prompting module. The former is employed for image restoration, whereas the latter encodes weather-related degradation-specific information to modulate the restoration module, enhancing the recovery process for improved results. Inspired by the recent trend of prompt learning in artificial intelligence, this paper adopts soft-prompt technology to generate learnable visual prompt parameters for better perceiving the degradation-conditioned cues. Extensive experimental results on several benchmarks show that our approach achieves superior restoration performance in maritime image dehazing and deraining tasks.

Unmanned surface vehicles (USVs) offer significant value through their capability to undertake hazardous and time-consuming missions across water surfaces. Recently, as the application of USVs has been extended to nearshore waterways, object tracking is vital to the safe navigation of USVs in offshore scenes. However, existing tracking systems for USVs are mainly based on cameras or LiDAR sensors, which suffer from drawbacks such as lack of depth perception or high deployment costs. In contrast, millimeter-wave (MMW) radar offers advantages in terms of low cost and robustness in all weather and lighting conditions. In this work, to construct a robust and low-cost tracking system for USVs in complex offshore scenes, we propose a novel MMW radar-based object tracking method (ROTracker). The proposed ROTracker combines the physical properties of MMW radar with traditional tracking systems. Specifically, we introduce the radar DOPPLER velocity and a designed motion discriminator to improve the robustness of the tracking system toward low-speed targets. Moreover, we conducted real-world experiments to validate the efficacy of the proposed ROTracker. Compared to other baseline methods, ROTracker achieves excellent multiple object tracking accuracy in terms of 91.9% in our collected dataset. The experimental results demonstrated that the proposed ROTracker has significant application potential in both accuracy and efficiency for USVs, addressing the challenges posed by complex nearshore environments.

NOAA Cautions Boaters to Give Right Whales Space.

Hundreds of migrants turned away by Italyarrive in Spain.

Carnival, Royal Caribbean, and Norwegian Cruise Line are banning people who have been to China recently from sailing on their ships to protect passengers from the coronavirus.

Vingt marins irakiens retrouvés morts après un récent naufrage.

Report on 14 Large Whales That Died due to Ship Strikes off the Coast of Sri Lanka, 2010-2014.

The maritime shipping industry has been an essential part of global trade for centuries. It plays a crucial role in the transportation of goods across oceans and connecting different parts of the world. The shipping industry has gone through various changes in recent years, and as a result, it has become more complex and competitive. In this article, we will provide an in-depth analysis of the maritime shipping industry, highlighting its current state, challenges, and opportunities.