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Although side-scan sonar can provide wide and high-resolution views of submarine terrain and objects, it suffers from severe interference due to complex environmental noise, variations in sonar configuration (such as frequency, beam pattern, etc.), and the small scale of targets, leading to a high misdetection rate. These challenges highlight the need for advanced detection models that can effectively address these limitations. Here, this paper introduces an enhanced YOLOv9(You Only Look Once v9) model named SOCA-YOLO, which integrates a Small Object focused Convolution module and an Attention mechanism to improve detection performance to tackle the challenges. The SOCA-YOLO framework first constructs a high-resolution SSS (sidescan sonar image) enhancement pipeline through image restoration techniques to extract fine-grained features of micro-scale targets. Subsequently, the SPDConv (Space-to-Depth Convolution) module is incorporated to optimize the feature extraction network, effectively preserving discriminative characteristics of small targets. Furthermore, the model integrates the standardized CBAM (Convolutional Block Attention Module) attention mechanism, enabling adaptive focus on salient regions of small targets in sonar images, thereby significantly improving detection robustness in complex underwater environments. Finally, the model is verified on a public side-scan sonar image dataset Cylinder2. Experiment results indicate that SOCA-YOLO achieves Precision and Recall at 71.8% and 72.7%, with an mAP50 of 74.3%. It outperforms the current state-of-the-art object detection method, YOLO11, as well as the original YOLOv9. Specifically, our model surpasses YOLO11 and YOLOv9 by 2.3% and 6.5% in terms of mAP50, respectively. Therefore, the SOCA-YOLO model provides a new and effective approach for small underwater object detection in side-scan sonar images.

- New analysis from the Mærsk Mc-Kinney Møller Center for Zero Carbon Shipping (MMMCZCS) shows that only 33 out of 94 (35%) of the major shipping companies have a clearly expressed target to be net zero by latest 2050 and/or have committed to IMO targets of 50% absolute reduction in 2050 compared to the 2008 level - It is a serious wakeup call for the industry, and the MMMCZCS urges more shipowners to lead the way towards a more sustainable maritime industry - Companies should set emissions reduction targets, and back up pledges with details on their strategy of how to get there

The Artificial Potential Field (APF) algorithm has been widely used for collision avoidance on unmanned ships. However, traditional APF methods have several defects that need to be addressed. To ensure safe navigation with good seamanship and full compliance with the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGS), this study proposes a dynamic collision avoidance method based on the APF algorithm. The proposed method incorporates a ship domain priority judgment encounter situation, allowing the algorithm to perform collision avoidance operations in accordance with actual operational requirements. To address path interference and unreachable target issues, a new attractive potential field function is introduced, dividing the attractive potential field of the target point into multiple segments simultaneously. Additionally, the repulsive force on the own ship is reduced when close to the target point. The results show that the proposed method effectively resolves path oscillation problems by integrating the potential field based on traditional APF with partial ideas from the Dynamic Window Approach (DWA). In comparison with traditional APF algorithms, the overall smoothing degree was improved by 71.8%, verifying the effectiveness and superiority of the proposed algorithm.

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.

Piracy in West Africa Targets the Region's Oil Industry.

Piracy in West Africa Targets the Region's Oil Industry.

Paris Talks to Target Shipping Emissions.

Paris Talks to Target Shipping Emissions.

Pirates shift tactics to target crews, not cargo.

ReCAAP: Abu Sayyaf Now Targeting Merchant Ships.

China to Target Overcapacity in Shipping.

Coastal security: NIMASA targets 100% piracy free 2017.

CO2 targets should cover shipping emissions-adviser.

Cairo: Today marks exactly one month until the official opening of the newly expanded Suez Canal, and sources in Cairo, the US and Europe warn Splash that Islamic State is intending to target the waterway as it pushes further into Egypt.

China targets ship pollution, spurs ECA conjecture.

China targets ship pollution, spurs ECA conjecture.

Syriza targets to stop privatization of Piraeus Port.

Syriza targets to stop privatization of Piraeus Port.

Al Qaeda targets oil tankers in Gibraltar.

Arctic Cruise Operators Target Plastic Use.

Tianjin Targeting 800,000 Passengers in 2017.

Global Cruise Industry Targeting 40 Million-Plus Passengers.

A voluntary commercial vessel slowdown trial was conducted through 16 nm of shipping lanes overlapping critical habitat of at-risk southern resident killer whales (SRKW) in the Salish Sea. From August 7 to October 6, 2017, the trial requested piloted vessels to slow to 11 knots speed-through-water. Analysis of AIS vessel tracking data showed that 350 of 951 (37%) piloted transits achieved this target speed, 421 of 951 (44%) transits achieved speeds within one knot of this target (i.e., ≤12 knots), and 55% achieved speeds ≤ 13 knots. Slowdown results were compared to 'Baseline' noise of the same region, matched across lunar months. A local hydrophone listening station in Lime Kiln State Park, 2.3 km from the shipping lane, recorded 1.2 dB reductions in median broadband noise (10-100,000 Hz, rms) compared to the Baseline period, despite longer transit. The median reduction was 2.5 dB when filtering only for periods when commercial vessels were within 6 km radius of Lime Kiln. The reductions were highest in the 1st decade band (-3.1 dB, 10-100 Hz) and lowest in the 4th decade band (-0.3 dB reduction, 10-100 kHz). A regional vessel noise model predicted noise for a range of traffic volume and vessel speed scenarios for a 1133 km2 'Slowdown region' containing the 16 nm of shipping lanes. A temporally and spatially explicit simulation model evaluated the changes in traffic volume and speed on SRKW in their foraging habitat within this Slowdown region. The model tracked the number and magnitude of noise-exposure events that impacted each of 78 (simulated) SRKW across different traffic scenarios. These disturbance metrics were simplified to a cumulative effect termed 'potential lost foraging time' that corresponded to the sum of disturbance events described by assumptions of time that whales could not forage due to noise disturbance. The model predicted that the voluntary Slowdown trial achieved 22% reduction in 'potential lost foraging time' for SRK

Shipping Giants May Miss Climate Targets.