Conventional multimodal alignment and fusion methods struggle to adapt to complex underwater detection environments. Owing to the divergences in imaging mechanisms between acoustic sonar and optical cameras, cross-modal features are often spatially misaligned, resulting in degraded fusion performance. This issue significantly limits the accuracy of underwater target detection in turbulent and turbid marine environments. To address these challenges, this study proposes an acoustic-optical multimodal fusion detection architecture for underwater perception tasks.

An end-to-end five-layer detection framework is developed, comprising an input layer, a feature extraction layer, a multimodal fusion layer, a target perception layer, and output layers. Two independent ResNet50 branches are employed to extract multi-scale feature representations from sonar and optical images, respectively. A novel spatial alignment module is designed to estimate affine transformation parameters, including scaling and translation factors, enabling pixel-level spatial registration between modalities. Integrated with channel and spatial attention mechanisms, the dynamic weighted fusion module adaptively adjusts the contribution of each modality, thereby suppressing low-quality and noisy features. Furthermore, a hierarchical interactive fusion encoder incorporating DenseNet and a cross-attention mechanism is constructed to achieve deep complementary fusion of multi-scale cross-modal features. The Transformer decoder and Hungarian matching loss inherited from DETR are utilized for end-to-end target classification and bounding-box regression, eliminating the need for additional non-maximum suppression operation.

Comparative and ablation experiments are conducted on a self-constructed real-world paired acoustic-optical underwater dataset. The proposed method achieves an mAP₅₀ of 95.6% and an mAP_50-95 of 50.7%, consistently outperforming state-of-the-art unimodal detectors (YOLOv12-X, YOLOv13-X, RTDETR) and advanced multimodal fusion methods (DenseFusion, U2Fusion, SwinFusion). Ablation studies confirm the critical contributions of both the spatial alignment module and the dynamic weighted fusion module to overall detection performance. In addition, noise injection experiments demonstrate that the dynamic weighting strategy exhibits strong robustness against speckle noise and random pixel occlusion in challenging underwater environments.

The proposed framework effectively mitigates cross-modal spatial misalignment and fusion degradation between sonar and optical imagery, resulting in significant improvements in underwater target detection accuracy. It delivers a feasible fusion paradigm for underwater multimodal perception.

To address issues such as unclear application architecture, weak information interaction capability, and low efficiency in the collaborative testing of heterogeneous marine unmanned clusters, this paper proposes an agile collaboration technology. This approach enables system-level clusters to quickly respond to task demands platform-level unmanned system to integrate swiftly into the cluster, and system-level controllers decouple software and hardware step-by-step for agile task processing and execution.

First, this study analyzes the task characteristics, network and optimization requirements for the cooperative application scenarios of marine unmanned clusters, and divides them into several node groups according to their functions. Then, it can design the heterogeneous marine unmanned cluster agile cooperative architecture based on the functional node groups, and carry out the load complementation and task coordination through the fusion configuration of the cooperative architecture. Next, based on the application requirements and architectural features, the common marine unmanned cluster application tasks are divided into three categories: time priority tasks, sequential execution tasks, and routine operation tasks, and an agile task planning method based on the self-organizing graph algorithm is proposed for the heterogeneous marine unmanned clusters. Finally, a multilevel software-hardware decoupled marine unmanned cluster agile collaborative controller is developed, which interacts with the various systems of the platform in the form of a unified interface.

According to the test results of dynamic target detection and tracking on-lake experiments by heterogeneous unmanned clusters, the final average heading deviation angle between each vehicle and the target is 6.7°, which successfully accomplished the cooperative detection and tracking of dynamic targets.

This method can enable marine unmanned systems with significant performance disparities to quickly integrate into and implement typical tasks, and has excellent scalability and adaptability, which can help accelerate the development and practice of marine unmanned clusters.

Wireless sensor networks (WSNs) are essential for ocean monitoring and are widely used in environmental monitoring, target localization, marine resource development, and disaster warning applications. However, WSNs often face challenges such as arbitrary deployment strategies, low effective coverage, and high coverage redundancy, which degrade network performance. To address these issues, this paper proposes a fractional-order chameleon swarm algorithm (FCSA) to optimize the deployment of static WSN nodes.

First, an improved Circle chaotic mapping method is employed to enhance population diversity and global distribution, ensuring higher-quality initial conditions for optimization. Next, during the velocity update phase, a fractional-order velocity update strategy is introduced to effectively leverage the historical search experiences of individuals, enhancing the balance between global exploration and local exploitation. Furthermore, the Levy flight mechanism is incorporated into position updates, providing stronger jumping characteristics and adaptability. These improvements enable FCSA to effectively optimize key performance indicators such as coverage rate and coverage redundancy, significantly enhancing deployment efficiency and distribution uniformity for static WSN nodes while ensuring better adaptability to complex environments.

Simulation results demonstrate that FCSA outperforms CSA, CSA-Circle, CSA-Levy, GA, RSO, and eleven other classical optimization algorithms in static node deployment. FCSA achieves a high coverage rate of 0.8018 while significantly reducing coverage redundancy to 0.0078. Additionally, for single optimization tasks, FCSA exhibits the fastest convergence, requiring only 638 iterations to reach a fitness value of 0.191409, significantly outperforming other algorithms. After 30 independent runs, statistical analysis shows that FCSA maintains an extremely fast convergence speed in the early iterations, reaching an optimal fitness value of 0.198222 after 1000 iterations. Among the ten algorithms, FCSA is the only one with a standard deviation of the fitness value below 0.2, indicating superior global search ability, higher convergence accuracy, and better distribution uniformity. It effectively mitigates the issue of uneven node distribution observed in traditional algorithms while maintaining strong stability and robustness.

In addressing the WSN static node deployment problem, FCSA effectively optimizes sensor placement, significantly improving monitoring quality through a multi-strategy collaborative optimization approach. The algorithm exhibits strong robustness and adaptability in complex environments. Additionally, FCSA provides an efficient, high-quality deployment solution for ocean monitoring and similar applications, offering strong theoretical and technical support for sensor network optimization and expansion, with significant application potential and practical value.