Sonar imaging, due to its ability to penetrate water and certain obstacles, has become a pivotal technology in marine domains. The complexities of underwater environments, combined with the low resolution and noise interference inherent in sonar images, present significant challenges in sonar image segmentation. To address these issues, we propose a novel approach, GM-SAM, which integrates denoising and multi-scale boundary feature extraction into the Segment Anything Model (SAM). Initially, the Multi-Perspective Feature Module (MPFM) employs parallel attention mechanisms and channel-enhanced convolutions to reduce computational complexity, suppress noise, and capture both local details and global feature. Following this, the Gradient-Enhanced Transformation Module (GETM) leverages gradient-based operations to extract edge features, enhancing the model’s sensitivity to boundaries. Finally, the Sonar-Fusion Adapter module integrates task-specific knowledge from MPFM with general features from SAM. Experimental results demonstrate that GM-SAM significantly outperforms existing methods, achieving superior Dice scores and effectively segmenting valid targets.

The unique nature of map data presents challenges for detecting key error areas in problematic maps, especially in terms of discontinuous boundary feature extraction and neglect of small target information. To address these issues, we propose a lightweight problematic map detection algorithm called YOLO-Map. First, to ensure the integrity of edge extraction and resistance to interference, we designed the Dual-branch Attention Convolution Module (DACM), which utilizes the synergistic effect of two branches to accurately identify national boundary regions. Next, the Multi-Path Feature Aggregation (MPFA) module adopts a bidirectional adaptive fusion strategy, enhancing recursive connections of multi-scale features and improving target localization accuracy. Additionally, we propose the Global Context Fusion Module (GCFM), which strengthens small target feature representation through a multi-branch collaborative attention mechanism. Experimental results show that YOLO-Map achieves an accuracy of 87.3% ([email protected]) on the CME dataset, outperforming many larger models.

Repetitive Action Counting (RAC) is a critical and challenging task in video analysis, aiming to count the number of repeated actions in videos accurately. Existing methods typically generate a Temporal Self-similarity Matrix (TSM) as an intermediate representation to predict the number of repetitive actions. While this simplifies the process, it often overlooks the variable lengths between action cycles and the phenomenon of motion interruptions. The period inconsistency problem caused by the change in the action period and the motion interruption problem resulting from the motion pause are the two main challenges that affect the accuracy of RAC in complex scenes. To address these challenges, we propose a novel framework. First, we construct a boundary-aware encoder equipped with a temporal pyramid structure to build multi-scale video features, capturing the period information of different lengths of repetitive actions to solve the period inconsistency problem. Next, a cycle and boundary attention module is followed by each layer in the pyramid to enhance these multi-scale features with periodic and event boundary information. Finally, we design a gated density estimator to generate the actionness score for each frame that reflects the probability of the corresponding time point being within the motion cycle. These scores are used to weight features to reduce the impact of noise frames without actions present and solve the motion interruption problem for better density prediction. Extensive experiments conducted on public datasets demonstrate the effectiveness of our method. The source code will be available at https://github.com/zqzhang2023/TBANRAC .

Accurate measurement of wind speed on maritime vessels is crucial for weather and sea condition forecasting, safe navigation, power generation, hydrological simulation, and other applications. However, the precision of measurements may be subject to certain errors due to factors such as vessel motion and environmental conditions. To enhance the precision of shipborne wind speed measurement, this paper introduces an innovative approach based on contrastive learning. Through proficient feature extraction and the application of a self-supervised contrastive learning algorithm, this method extracts features of varying granularity from marine observational data to predict and correct shipborne wind speed measurements. To the best of our knowledge, this study represents the first attempt to validate contrastive learning in the intelligent analysis of marine observational data. Validation experiment results demonstrate the outstanding performance of this method in both single-step and multi-step predictions, showcasing higher efficacy and robustness compared to alternative approaches.

Underwater Image Restoration Technology (UIRT) constitutes a pivotal base for subsequent tasks yet confronts obstacles like data scarcity and image distortion in practical deployments. To optimize the use of scarce annotated and copious unlabeled data, we introduce an avant-garde semi-supervised method for underwater image restoration, named STUI-Net. Concurrently, we develop a novel teacher-student architecture utilizing Transformer technology, named GFT-Net. Initially, GFT-Net employs a tripartite branch network—comprising Gradient, Feature Extraction, and Transformer modules to thoroughly extract features from underwater images. This method then amalgamates multi-source features, encompassing edge, gradient, local, and global data, addressing underwater image deterioration in multifaceted environments. Additionally, we engineer a Supervisor role to rectify potential misdirection by the teacher network in the semi-supervised model, thereby enhancing training stability on unlabeled datasets. Empirical studies affirm the robust generalization competence of STUI-Net in authentic underwater environments.

The fidelity of underwater visual data is significantly affected by various factors, encompassing light refraction and color distortion in water. These factors often give rise to noise and distortion within underwater images. In response to the widespread nature of this issue, we propose a novel approach to restore authentic underwater images through a comprehensive perspective. To address the inherent scarcity of real underwater image datasets, we introduce an innovative underwater image generation model that leverages Generative Adversarial Networks (GAN) with integrated physical constraints, aiming to produce realistic and stable underwater image datasets. Within the underwater image restoration framework, we incorporate a multi-channel feature extractor (MCFE) module, which is designed to enhance the model’s capability in image feature extraction. Additionally, we introduce object edge information as a novel loss function to perform different repairs on the target and background. Experimental evaluations demonstrate that our method exhibits superior performance in both qualitative and quantitative assessments compared to state-of-the-art approaches. Restoration results on real underwater images further showcase its exceptional performance in practical applications. The source code and sample dataset are publicly available at here.

In recent years, accurate and real-time long-range small target detection has become a popular and challenging task, particularly in time-sensitive scenarios such as unmanned aerial vehicle (UAV) scene analysis and military reconnaissance. Most existing solutions rely on deep CNNs to learn strong feature representations of objects isolated from the background to detect small objects with minimal visual features in images. However, this approach incurs significant computational overhead. In this paper, we propose DualYOLO, a fast and accurate long-range small object detection method that combines multi-level multi-scale feature fusion (MLMFF) and concat channel attention (CatCA). Specifically, in order to prevent small targets from becoming more and more blurred after multilayer convolution operations, DualYOLO fuses the features of different layers in the backbone network to obtain small target features with strong semantics and high detail. Furthermore, we use a new loss function to address the sensitivity of IoU to small object position deviations, thereby improving detection accuracy. In terms of data preprocessing, we utilize an image slicing strategy to process the dataset. The experimental results show that DualYOLO achieves 82.2% accuracy (in terms of [email protected]) on the VEDAI dataset processed using slices, with a performance more than 2% higher than that of large models (e.g., YOLOv5x,YOLOR and YOLOv7).