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Oil company ships out first 20 million tons through Arctic waters, says much more is in the pipeline.

On Friday, Israel's Ministry of Environmental Protection responded to a leak at the marine terminal of the Eilat-Ashkelon Pipeline. According to the agency, a spill of several cubic meters of light crude oil was released when an "emergency detachment accessory" of the terminal malfunctioned.

- Cross-border shipping of CO2 can play a key role in the Carbon Capture, Utilisation and Sequestration (CCUS) value chain, particularly when sequestration opportunities near emitters are limited. - By 2050, Asia Pacific (APAC) countries could require up to 150 vessels to transport an estimated 100 million tons per annum (MtPA) of captured CO2 across national borders. - Shipping is more cost-effective than offshore pipelines when transporting 5 MtPA or less of captured CO2 over a distance of 500 km or more.

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.