To visually detect sea-surface targets, the objects of interest must be effectively and rapidly isolated from the background of sea-surface images. In contrast to traditional image detection methods, which employ a single visual feature, this paper proposes a significance detection algorithm based on the fusion of multi-visual features after detecting the sea-sky-lines. The gradient edges of the sea-surface images are enhanced using a Gaussian low-pass filter to eliminate the effect of the image gradients pertaining to the clouds, wave points, and illumination. The potential region and points of the sea-sky-line are identified. The sea-sky-line is fitted through polynomial iterations to obtain a sea-surface image containing the target object. The saliency subgraphs of the high and low frequency, gradient texture, luminance, and color antagonism features are fused to obtain an integrated saliency map of the sea-surface image. The saliency target area of the sea surface is segmented. The effectiveness of the proposed method was verified. The average detection rate and time for the sea-sky-line detection were 96.3% and 1.05 fps, respectively. The proposed method outperformed the existing saliency models on the marine obstacle detection dataset and Singapore maritime dataset, with mean absolute errors of 0.075 and 0.051, respectively.

To address the problem of unclear images affected by occlusion from fog, we propose an improved Retinex image enhancement algorithm based on the Gaussian pyramid transformation. Our algorithm features bilateral filtering as a replacement for the Gaussian function used in the original Retinex algorithm. Operation of the technique is as follows. To begin, we deduced the mathematical model for an improved bilateral filtering function based on the spatial domain kernel function and the pixel difference parameter. The input RGB image was subsequently converted into the Hue Saturation Intensity (HSI) color space, where the reflection component of the intensity channel was extracted to obtain an image whose edges were retained and are not affected by changes in brightness. Following reconversion to the RGB color space, color images of this reflection component were obtained at different resolutions using Gaussian pyramid down-sampling. Each of these images was then processed using the improved Retinex algorithm to improve the contrast of the final image, which was reconstructed using the Laplace algorithm. Results from experiments show that the proposed algorithm can enhance image contrast effectively, and the color of the processed image is in line with what would be perceived by a human observer.

Textured surfaces have been widely applied in the field of engineering for friction reduction, but research on the effects of different texture design parameters on the wear and fatigue resistance of bearing steel remains insufficiently explored. In order to improve the anti-wear and anti-fatigue performance and prolong the service life of bearing steel, surface texturing technology was applied to fabricate textured GCr15 bearing steel materials. In this work, micro-groove surface texture was designed by hydrodynamic theory, then, it was fabricated on the GCr15 bearing steel surface using nanosecond laser processing. The depths (15 μm, 30 μm), spacing (100 μm, 200 μm, 300 μm), and friction angles (30°, 60°, 90°) of micro groove were selected as the primary parameters for micro groove fabrication. The friction and wear tests and rolling contact fatigue (RCF) tests were conducted to investigate the lubrication tribological properties. The results indicated that micro-groove spacing of 300 μm significantly reduces the coefficient of friction (CoF), depth of 30 μm produces a higher hydrodynamic pressure, and friction angle of 60° has a lower CoF value. Compared to the untextured samples, the RCF life of micro-groove surface texture sample is improved, and the median RCF life is increased by 36.5%. And it could be attributed to the dispersion effect of micro-groove surface texture on stress concentration. The findings indicate that a well-designed micro-groove structure can enhance the fluid dynamic pressure lubrication effect to a certain degree. In comparison to smooth surface specimens, those with micro-grooves exhibit reduced friction.

Double-head machine tool has the advantages of high efficiency and high degree of automation. In order to reduce the weight of double-head machine tool and improve the stiffness of the entire machine. An optimization design method combining topology optimization, sensitivity analysis and adaptive multi-objective method is used. Firstly, simplify the model in SolidWorks and import it into ANSYS Workbench software to carry out finite element analysis on the entire double-head machine tool to find out the weak component as the beam. Afterwards, carry out topological optimization on the beam and redesign the beam structure, and complete the first optimization. Then, through sensitivity analysis of the input parameters, key parameters that significantly impact the objective function are identified. Subsequently, a multi-objective optimization function is constructed for these key parameters and the objective function. Finally, an adaptive multi-objective method is used to solve the problem and obtain a Pareto optimal solution set, completing the second optimization. The results show that the weight of the beam is reduced by 8.88 %, the deformation of the beam is reduced by 11.29 %, and the equivalent stress of the entire machine is reduced by 28.33 %. This design not only yields significant economic benefits but also serves as a valuable reference for the lightweight design of large machine tool crossbeams.

Reliable ship detection in complex maritime optical imagery is a fundamental requirement for intelligent maritime monitoring and maritime automation systems. However, severe image degradation, large-scale variations, and background clutter often lead to feature ambiguity and unstable detection performance in real-world maritime environments. To address these challenges, this paper proposes a lightweight one-stage ship detection framework designed for robust real-time perception under degraded maritime sensing conditions. The proposed method incorporates an Adaptive Expert Selection Attention (AESA) mechanism to perform adaptive feature selection and background suppression under visually degraded conditions, together with a Geometry-Aware MultiScale Fusion (GAMF) module that enables orientation-aware aggregation of contextual information for elongated ship targets near complex sea–sky boundaries. In addition, a geometry-aware bounding box regression refinement is introduced to improve localization consistency in image space. Extensive experiments conducted on a unified real-world maritime benchmark demonstrate that the proposed framework consistently outperforms the baseline YOLO11n model by approximately 2–5 percentage points in terms of mAP@0.5 and mAP@0.5:0.95, while maintaining moderate computational complexity and real-time inference capability. These results indicate that the proposed method provides a practical and deployment-oriented perception solution for maritime automation applications, including onboard electro-optical sensing and coastal surveillance.