Search Results (136)

The search and discovery of underwater shipwreck sites represent the most arduous and critical phases of underwater archaeology. Wooden shipwrecks, in particular, are characterized by low acoustic impedance contrast and weak magnetic anomalies, coupled with their limited physical dimensions. Consequently, they predominantly exist as shallow-buried, discontinuous small targets scattered within confined areas, making their detection exceptionally challenging. Furthermore, the complexity of the submarine environment—including rugged topography, turbid water columns, and strong currents—poses formidable obstacles to the effective detection of these archaeological remains. Single geophysical methods are often limited by insufficient imaging resolution, interpretation ambiguity, and geological noise, making precise localization and characterization difficult. Focusing on the Nan’ao I Ming Dynasty shipwreck located in waters approximately 24 m deep off the coast of Nan’ao, Guangdong Province, China, this study proposes and validates an “acoustic-magnetic” multi-source data integration detection method. This approach systematically integrates high-resolution multibeam echo sounding (MBES), side-scan sonar (SSS), sub-bottom profiling (SBP), and marine magnetic data to establish a comprehensive framework for identification and integration analysis. The results indicate that the MBES bathymetric data reveal a regular, elongated structure oriented north–south (approximately 34 m × 12 m), closely matching the main hull and deck configuration. The SSS imagery exhibited high backscatter intensity and parallel linear textures, effectively delineating the hard shipwreck structure and the associated rigid protective frame employed for in situ preservation. SBP data confirmed the semi-buried state of the shipwreck (burial depth of approximately 0.6 m). Spatial variations in sediment thickness around the site suggested ongoing modification by strong hydrodynamic processes. Marine magnetic surveys identified localized negative anomalies (−210 nT relative to the ambient magnetic field), contrasting sharply with the positive anomalies of the surrounding natural reefs, thereby indicating an artificial ferromagnetic source. The spatial registration and feature superposition of multi-source data facilitated the characterization of the shipwreck, demonstrating its potential to mitigate environmental interference and enhance detection reliability in this complex environment. Using the Nan’ao I shipwreck site as a case study, this study provides a detailed characterization of the site’s 3D morphology, burial state, and physical properties. The proposed methodology offers a practical and robust technical solution for underwater shipwreck archaeology in complex nearshore environments, providing significant implications for proactive discovery, efficient investigation, and protection of underwater cultural heritage (UCH). Full article

The preservation of submerged cultural heritage depends on the ability to locate, document, and monitor sites before they are degraded or lost. Although the North American Great Lakes contain thousands of exceptionally well-preserved shipwrecks, their large geographic extent and diverse operating environments present significant challenges for efficient archeological survey. This study presents a multi-platform autonomous survey framework developed and implemented during 2021–2022 field campaigns in Lake Michigan and Lake Ontario. The framework integrates autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), crewed vessels, side-scan sonar, multibeam bathymetry, magnetometry, optical imaging, and field-based data review within a hierarchical workflow comprising wide-area assessment (WAA) reconnaissance, high-resolution geophysical (HRG) mapping, adaptive mission refinement, and visual confirmation. The surveys produced 19.72 km² of geophysical coverage, including side-scan sonar mosaics, bathymetric surfaces, magnetic anomaly maps, and optical imagery that supported archeological interpretation. A case study from Lake Ontario demonstrates the framework’s effectiveness through the confirmation of a previously undocumented wooden shipwreck using complementary acoustic, magnetic, and visual datasets. Beyond the individual discoveries, the results demonstrate how integrated autonomous systems improve survey efficiency, support adaptive decision-making, and provide scalable methods for digital documentation, baseline site characterization, long-term monitoring, and preservation of submerged cultural heritage in freshwater and marine environments. Full article

Accurate target detection in side-scan sonar imagery is important for marine resource investigation, underwater infrastructure inspection, and maritime security. However, Side-scan sonar images are often affected by low contrast, acoustic speckle, weak target boundaries, and cluttered seabed backgrounds, which make target detection particularly challenging, especially under limited training data conditions. To improve the representation of sonar-specific structures, this study proposes a multi-feature early fusion detection network, referred to as MFEF-Det, for side-scan sonar target detection. The method combines multiple handcrafted features with data-driven representations to provide complementary information. In particular, a directional contrast core response feature (DCCR) is introduced to better emphasize the echo–shadow structure commonly observed in side-scan sonar imagery. An adaptive fusion strategy is then adopted to combine multiple feature maps before feeding them into a detection network, and an attention refinement module is further employed for complex scenes to improve the discrimination between target-related regions and cluttered backgrounds. Experiments were conducted on two publicly available sonar datasets. Experiments on the KLSG and SSS-Bottom datasets demonstrate that MFEF-Det achieves 0.933 ± 0.016 mAP@0.5 and 0.866 ± 0.052 mAP@0.5, respectively. The results indicate that the proposed feature representation can improve detection performance in both relatively clean and more challenging noisy scenes. These findings suggest that incorporating sonar-specific priors can be beneficial for side-scan sonar detection in marine survey and maritime-security applications. Full article

Underwater small target detection in side scan sonar (SSS) imaging remains a difficult problem. In this paper, we propose an improvement method based on YOLO (You Only Look Once) by tackling two critical issues: preserving fine features of small targets and suppressing background interference. Our specific contributions are twofold: (i) we incorporate shallow layer feature maps to retain spatial details that are essential for small objects, and (ii) we introduce an attention mechanism into the feature fusion stage to improve discriminative ability against seafloor background. This improved method is verified by the public data collected by Portugal government, which achieves a precision of 83.6%, thereby enabling more accurate target detection. Experimental results proved the effectiveness of the proposed method, which can be applied in the underwater small target detection field. Full article

Side-scan sonar images play an important role in underwater target detection, seabed mapping, and marine environment monitoring. However, the performance of deep learning-based detectors is often limited by the small scale of available sonar datasets, the high cost of data acquisition, and class imbalance among target categories. To address these issues, this paper proposes a diffusion-model-based data augmentation method for side-scan sonar target detection. A FLUX.1 diffusion model is adopted as the base generative framework and is fine-tuned using low-rank adaptation (LoRA) to adapt the pretrained model to the side-scan sonar image domain under limited training data conditions. The generated samples are further filtered and added only to the training set, while the validation and test sets are kept unchanged and contain only real sonar images. To ensure a fair evaluation of the augmentation strategy, all detection experiments are conducted using a fixed YOLOv8n (You Only Look Once version 8 nano) detector under the same training hyperparameters and three random seeds. Compared with training on the original dataset, the proposed FLUX+LoRA augmentation improves mean average precision (mAP)@0.5 from 0.7400 ± 0.0132 to 0.8582 ± 0.0328 and mAP@0.5:0.95 from 0.3994 ± 0.0187 to 0.5115 ± 0.0164. It also outperforms conventional augmentation methods under the same real-only validation/test protocol. In addition, Fréchet Inception Distance (FID)/Kernel Inception Distance (KID)-based image quality evaluation, generated-sample amount ablation, screening-strategy ablation, LoRA-rank sensitivity analysis, and a controlled 600-sample diffusion-backbone comparison are conducted. The results show that the 600-sample manually annotated FLUX+LoRA subset selected from generated samples achieves better image quality and detection performance than FLUX-base and SD1.5+LoRA under the same annotation budget. These findings demonstrate that FLUX+LoRA-generated sonar images can provide useful structural diversity for detector training and improve target detection performance under limited-data conditions. Full article

Ultra-shallow and confined water environments are challenging to survey with conventional towed side-scan sonar (SSS) due to limited access and positioning uncertainties. This study introduces a portable, battery-powered acoustic survey system that integrates a pole-mounted dual-frequency side-scan sonar (600/1600 kHz) with RTK GNSS (Real-Time Kinematic Global Navigation Satellite System), deployable from a small inflatable boat. The system was validated in two settings: an inland lake and a marina. Field trials demonstrated reliable acquisition of high-resolution sonar imagery and effective detection of both natural and anthropogenic seabed features, including small and low-reflectivity objects. The high-frequency channel (1600 kHz) produced superior image quality and interpretability compared to the lower frequency. While there are limitations associated with fixed sonar mounting and limited altitude control, the system offers high mobility, rapid deployment, and operational safety. This approach represents a practical, cost-effective solution for high-resolution acoustic remote sensing in ultra-shallow water settings where traditional survey methods are ineffective or impractical. Full article

Side-scan sonar is a critical instrument for underwater cultural heritage preservation, as it allows large-scale detection of shipwrecks in turbid waters where optical methods fail. However, the automated segmentation of these targets remains a significant challenge, as severe speckle noise and complex seabed reverberations often obscure the distinctive geometric features of submerged structures. To address this challenge, this paper proposes SW-Net, which utilizes a multi-scale input strategy and a novel Directional Filter Bank to inject physical priors into the feature extraction process. Furthermore, by coupling this with a directional attention mechanism, the network dynamically modulates structural features to accurately segment targets despite intensity inversions and speckle noise. As demonstrated by the experimental results on the AI4Shipwrecks dataset, the SW-Net outperforms seven representative segmentation architectures, achieving the highest intersection over union of 39.43% and an F1-score of 56.56%. In addition, the model exhibits superior robustness against complex seabed interference while maintaining the lowest computational complexity of 4.01 million parameters among the evaluated methods. Taken together, the SW-Net is proposed to offer a practical solution for shipwreck detection on resource-constrained autonomous underwater vehicles. Full article

The topographic features of the seafloor can be observed clearly via high-resolution side-scan sonar imagery. However, the faithful interpretation of a sonar image depends strongly on the accuracy with which the location of the sea bottom line can be tracked within the image, and current tracking methods function poorly under high sonar signal noise or suffer from high complexity. The present work addresses this issue by applying the characteristics of simple sonar waterfall maps in conjunction with robust edge detection and multi-scale analysis based on wavelet transform modulus maxima. The proposed tracking method is demonstrated to provide superior effectiveness and accuracy in comparison with existing baseline methods based on the results of experiments conducted with a representative side-scan sonar image with and without applied speckle noise. This superiority can be attributed to the good localization characteristics and multi-scale detection features of wavelet transform analysis, which can suppress the impact of noise in the sonar image on the accurate extraction of edge information. Full article

Side-scan sonar (SSS) imaging plays a critical role in underwater perception for autonomous underwater vehicles (AUVs). However, the spatial sparsity of targets and the limited computational resources remain challenging for real-time object detection. Existing methods typically adopt dense inference strategies, leading to substantial computational redundancy and limited deployment feasibility. In this work, we propose a lightweight and ultra-fast SSS object detection framework based on target presence awareness. The proposed framework follows a coarse-to-fine inference paradigm, in which a target presence analysis module is first employed to rapidly filter out target-absent image patches, and only target-positive patches are forwarded to an Object Forward Detection (OFD) module for fine-grained detection. The TPA module integrates spatial–frequency convolution to efficiently capture both local structural cues and global contextual information with minimal computational overhead. Furthermore, an AttnConv-enhanced detection module is introduced in the OFD stage to strengthen high-frequency target features and improve fine-grained detection performance. Extensive experiments on public SSS datasets demonstrate that the proposed method achieves an mAP of 74.63% on the AI4Shipwrecks dataset and 63.02% on the SSS-Mine dataset. Notably, the framework delivers an ultra-fast inference speed of 174.74 FPS on embedded hardware, representing a 5.2× speedup over conventional dense-processing detection methods. Full article

Derelict crab pots (“ghost pots”) cause bycatch mortality, habitat degradation, and lost harvest in shallow coastal ecosystems. Existing detection and recovery programs rely on expert operators and high-cost sonar, limiting coverage and reproducibility. Here, we present GhostVision, an open-source framework that integrates low-cost consumer side-scan sonar with modern object-detection models to enable scalable, rapid post-processing and mapping of derelict gear. Mobile Mapping Units (MMUs) equipped with off-the-shelf fishfinders surveyed more than 1500 acres in Delaware’s Inland Bays between 2020 and 2022. Three architectures (YOLOv12, YOLOv26, RF-DETR) were trained on 3110 manually annotated sonar images and evaluated with both dataset-centric metrics and full pipeline implementation. YOLOv12 showed the strongest untuned operational performance (F1 = 0.512; recall = 0.922), while post-processing optimization produced comparable performance across all three models (F1 ≈ 0.71–0.73). Across 11 complete test recordings, end-to-end processing required only 8.87–9.79% of survey time (approximately 10–11× faster than real-time), supporting same-day analysis and recovery workflows. GhostVision can foster community engagement in derelict crab-pot removal by pairing low-cost sonar with AI to aid recovery efforts at management-relevant scales. By lowering financial and technical barriers, GhostVision provides a reproducible pathway for large-scale stewardship and supports future extensions to multi-class detection and autonomous platforms. Full article

Submarine gas emissions represent a key expression of fluid migration processes in volcanic and hydrothermal marine environments and provide valuable analogues for monitoring strategies relevant to sub-seabed carbon storage. This study investigates the feasibility of using marine Distributed Acoustic Sensing (DAS) to detect natural CO₂ bubble emissions in a shallow-water setting offshore Panarea (Aeolian Islands, Italy). A 1.1 km armored fiber-optic cable was deployed on the seabed and interrogated using two different DAS systems to acquire continuous passive acoustic data. The DAS recordings were complemented by controlled gas releases from scuba tanks to provide reference signals, as well as by independent high-resolution boomer seismic survey and side-scan sonar imaging to characterize the shallow subsurface and seabed morphology. The results show that DAS is sensitive to acoustic signals associated with both artificial and natural bubble emissions, despite the complex acoustic conditions typical of shallow marine environments. The integration of passive DAS monitoring with independent geophysical observations provides a robust framework for interpreting gas-related signals and seabed processes. These findings demonstrate that marine DAS represents a promising geophysical tool for monitoring of submarine volcanic–hydrothermal systems and offers important insights for the development of sub-seabed CO₂ leakage detection in offshore CCS contexts. Full article

With advancements in Autonomous Underwater Vehicle (AUV) and sensor technologies, the operational paradigms for seabed survey are undergoing significant transformation. Compared to traditional towed or remotely operated platforms, AUV-based seabed survey systems demonstrate superior capabilities in data resolution, operational efficiency and stealth. Furthermore, propelled by progress in artificial intelligence, the technical approaches of AUV-based seabed exploration systems are also experiencing disruptive changes. Based on our observations, existing review articles predominantly focus on individual technologies within seabed survey operations, failing to reflect the systemic constraints and interdependencies among these discrete technological components. This review focuses on the scenario of seabed target detection within seabed survey operations, summarizing research progress aimed at enhancing the effectiveness of such systems across three key technical areas: image processing of side-scan sonar (SSS) systems, intelligent detection of seabed targets and autonomous path planning for survey missions, which is based on a representative system—AUV-mounted SSS system. Given the multi-faceted challenges still present in seabed exploration technology, this paper aims to provide directional guidance for new researchers entering this field. Full article

To enable timely, effective, and high-accuracy detection of scour around offshore wind turbine pile foundations, this study proposes a three-dimensional reconstruction and scour volume detection method based on side-scan sonar imagery. First, the sonar images of pile foundations are preprocessed through grayscale conversion, binarization, and region expansion and merging to obtain an effective grayscale representation of scour pits. An optimized Shape-from-Shading (SFS) method is then applied to reconstruct the three-dimensional geometry from the effective grayscale map, generating point cloud data of the scour pits. Subsequently, the point cloud data are filtered using curvature and normal vector constraints, followed by depth-based z-axis descent detection, clustering, and morphological restoration to extract individual scour pit point clouds. Finally, a weight-corrected AlphaShape algorithm is employed to accurately calculate the volume of each scour pit. Numerical experiments involving five simulated scour scenarios across three types demonstrate that the proposed method achieves accurate identification and extraction of scour pit point clouds, with an average volume measurement accuracy of 97.495% compared with theoretical values. Field measurements in real-world environments further validate the effectiveness of the proposed method for practical scour volume detection around offshore wind turbine foundations. Full article

Automatic Target Recognition (ATR) in single-view sonar imagery is severely hampered by geometric distortions, acoustic shadows, and incomplete target information due to occlusions and the slant-range imaging geometry, which frequently give rise to misclassification and hinder practical underwater detection applications. To address these critical limitations, this paper proposes a Multi-View Deep Convolutional Neural Network (MVDCNN) based on feature-level fusion for robust sonar image target recognition. The MVDCNN adopts a highly modular and extensible architecture consisting of four interconnected modules: an input reshaping module that adapts multi-view images to match the input format of pre-trained backbone networks via dimension merging and channel replication; a shared-weight feature extraction module that leverages Convolutional Neural Network (CNN) or Transformer backbones (e.g., ResNet, Swin Transformer, Vision Transformer) to extract discriminative features from each view, ensuring parameter efficiency and cross-view feature consistency; a feature fusion module that aggregates complementary features (e.g., target texture and shape) across views using max-pooling to retain the most salient characteristics and suppress noisy or occluded view interference; and a lightweight classification module that maps the fused feature representations to target categories. Additionally, to mitigate the data scarcity bottleneck in sonar ATR, we design a multi-view sample augmentation method based on sonar imaging geometric principles: this method systematically combines single-view samples of the same target via the combination formula and screens valid samples within a predefined azimuth range, constructing high-quality multi-view training datasets without relying on complex generative models or massive initial labeled data. Comprehensive evaluations on the Custom Side-Scan Sonar Image Dataset (CSSID) and Nankai Sonar Image Dataset (NKSID) demonstrate the superiority of our framework over single-view baselines. Specifically, the two-view MVDCNN achieves average classification accuracies of 94.72% (CSSID) and 97.24% (NKSID), with relative improvements of 7.93% and 5.05%, respectively; the three-view MVDCNN further boosts the average accuracies to 96.60% and 98.28%. Moreover, MVDCNN substantially elevates the precision and recall of small-sample categories (e.g., Fishing net and Small propeller in NKSID), effectively alleviating the class imbalance challenge. Mechanism validation via t-Distributed Stochastic Neighbor Embedding (t-SNE) feature visualization and prediction confidence distribution analysis confirms that MVDCNN yields more separable feature representations and more confident category predictions, with stronger intra-class compactness and inter-class discrimination in the feature space. The proposed MVDCNN framework provides a robust and interpretable solution for advancing sonar ATR and offers a technical paradigm for multi-view acoustic image understanding in complex underwater environments. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article