The salient object detection (SOD) of forward-looking sonar images plays a crucial role in underwater detection and rescue tasks. However, the existing SOD algorithms find it difficult to effectively extract salient features and spatial structure information from images with scarce semantic information, uneven intensity distribution, and high noise. Convolutional neural networks (CNNs) have strong local feature extraction capabilities, but they are easily constrained by the receptive field and lack the ability to model long-range dependencies. Transformers, with their powerful self-attention mechanism, are capable of modeling the global features of a target, but they tend to lose a significant amount of local detail. Mamba effectively models long-range dependencies in long sequence inputs through a selection mechanism, offering a novel approach to capturing long-range correlations between pixels. However, since the saliency of image pixels does not exhibit sequential dependencies, this somewhat limits Mamba’s ability to fully capture global contextual information during the forward pass. Inspired by multimodal feature fusion learning, we propose a hybrid CNN–Transformer–Mamba architecture, termed FLSSNet. FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. The experimental results show that FLSSNet exhibits outstanding competitiveness among 25 state-of-the-art SOD methods, achieving MAE and ${\mathit{E}}_{\mathit{\xi }}$ values of 0.04 and 0.973, respectively.

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods.

With the rapid advancement of deep learning techniques, side-scan sonar image segmentation has become a crucial task in underwater scene understanding. However, the complex and variable underwater environment poses significant challenges for salient object detection, with traditional deep learning approaches often suffering from inadequate feature representation and the loss of global context during downsampling, thus compromising the segmentation accuracy of fine structures. To address these issues, we propose SonarNet, a Global Feature-Based Hybrid Attention Network specifically designed for side-scan sonar image segmentation. SonarNet features a dual-encoder architecture that leverages residual blocks and a self-attention mechanism to simultaneously capture both global structural and local contextual information. In addition, an adaptive hybrid attention module is introduced to effectively integrate channel and spatial features, while a global enhancement block fuses multi-scale global and spatial representations from the dual encoders, mitigating information loss throughout the network. Comprehensive experiments on a dedicated underwater sonar dataset demonstrate that SonarNet outperforms ten state-of-the-art saliency detection methods, achieving a mean absolute error as low as 2.35%. These results highlight the superior performance of SonarNet in challenging sonar image segmentation tasks.

Multi-board electronic cases with high-density and power modules are widely used in industrial power supply management. Heat dissipation becomes an important factor in the design process in improving case performance and miniaturization requirements. The design of existing small electronic thermal methods ignores high-temperature and high-load environment tests without automation control. To solve these problems, a heat dissipation case is designed with a magnesium and aluminum alloy, for intelligent temperature control based on a high-power and high-density power supply array. Based on the extreme miniaturization design principle, a composite heat dissipation mode is adopted based on conduction and supplemented by forced air cooling. The results show that the heat dissipation design in this article can work normally in high-temperature and high-load environment tests. Finally, the existing cooling designs are compared and analyzed. The cooling performance parameters in this article are better than those in the existing case. It contributes to the thermal design of miniaturized electronic cases in power supply management.

Recent advances in deep learning have shown exciting promise in various artificial intelligence vision tasks, such as image classification, image noise reduction, object detection, semantic segmentation, and more. The restoration of the image captured in an extremely dark environment is one of the subtasks in computer vision. Some of the latest progress in this field depends on sophisticated algorithms and massive image pairs taken in low-light and normal-light conditions. However, it is difficult to capture pictures of the same size and the same location under two different light level environments. We propose a method named NL2LL to collect the underexposure images and the corresponding normal exposure images by adjusting camera settings in the “normal” level of light during the daytime. The normal light of the daytime provides better conditions for taking high-quality image pairs quickly and accurately. Additionally, we describe the regularized denoising autoencoder is effective for restoring a low-light image. Due to high-quality training data, the proposed restoration algorithm achieves superior results for images taken in an extremely low-light environment (about 100× underexposure). Our algorithm surpasses most contrasted methods solely relying on a small amount of training data, 20 image pairs. The experiment also shows the model adapts to different brightness environments.

This paper introduces an event-triggered sigmoid prescribed performance control method, enhanced by an adaptive characteristic model, for tracking the trajectory of autonomous underwater vehicles (AUVs). The AUV model is simplified into a function reliant solely on second-order parameter information through the use of characteristic modeling and a compression algorithm, which is then approximated by a neural network. We propose integrating prescribed performance control into event-triggered sliding mode control to accelerate convergence in AUV trajectory tracking. A novel prescribed performance function is employed in this integration, creating an event-triggered, non-singular terminal sliding mode control strategy. The stability of this controller is rigorously proven. This control strategy is not only robust against model uncertainties but also mitigates the jitter commonly associated with sliding mode control and the singularities from preset performance control due to sudden random disturbances. Comparative simulation experiments demonstrate that the proposed control method achieves superior control accuracy and a quicker response.