Underwater target detection is a critical technology for marine resource management and ecological protection, but its performance is often limited by complex underwater environments, including optical attenuation, scattering, and dense distributions of small targets. Existing methods have significant limitations in feature extraction efficiency, robustness in class-imbalanced scenarios, and computational complexity. To address these challenges, this study proposes a lightweight adaptive detection model, YOLOv11-MSE, which optimizes underwater detection performance through three core innovations. First, a multi-scale dilated attention (MSDA) mechanism is embedded into the backbone network to dynamically capture multi-scale contextual features while suppressing background noise. Second, a Slim-Neck architecture based on GSConv and VoV-GSCSPC modules is designed to achieve efficient feature fusion via hybrid convolution strategies, significantly reducing model complexity. Finally, an efficient multi-scale attention (EMA) module is introduced in the detection head to reinforce key feature representations and suppress environmental noise through cross-dimensional interactions. Experiments on the underwater detection dataset (UDD) demonstrate that YOLOv11-MSE outperforms the baseline model YOLOv11, achieving a 9.67% improvement in detection precision and a 3.45% increase in mean average precision (mAP50) while reducing computational complexity by 6.57%. Ablation studies further validate the synergistic optimization effects of each module, particularly in class-imbalanced scenarios where detection precision for rare categories (e.g., scallops) is significantly enhanced, with precision and mAP50 improving by 60.62% and 10.16%, respectively. This model provides an efficient solution for edge computing scenarios, such as underwater robots and ecological monitoring, through its lightweight design and high underwater target detection capability.

Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key challenges such as the multi-scale characteristics of surface damage precursors, interference from background noise, and the scarcity of high-quality training samples severely constrain the intelligent transformation of AM quality monitoring systems. This study proposes an innovative microscopic polarization YOLOv11-LSF intelligent inspection framework, which establishes an automated non-destructive testing methodology for AM device micro-nano damage precursors through triple technological innovations, effectively breaking through existing technical bottlenecks. Firstly, a multi-scale perception module is constructed based on the Large Separable Kernel Attention mechanism, significantly enhancing the network’s feature detection capability in complex industrial scenarios. Secondly, the cross-level local network VoV-GSCSP module is designed utilizing GSConv and a one-time aggregation method, resulting in a Slim-neck architecture that significantly reduces model complexity without compromising accuracy. Thirdly, an innovative simulation strategy incorporating physical features for damage precursors is proposed, constructing a virtual and real integrated training sample library and breaking away from traditional deep learning reliance on large-scale labeled data. Experimental results demonstrate that compared to the baseline model, the accuracy (P) of the YOLOv11-LSF model is increased by 1.6%, recall (R) by 1.6%, mAP50 by 1.5%, and mAP50-95 by 2.8%. The model hits an impressive detection accuracy of 99% for porosity-related micro-nano damage precursors and remains at 94% for cracks. Its unique small sample adaptation capability and robustness under complex conditions provide a reliable technical solution for industrial-grade AM quality monitoring. This research advances smart manufacturing quality innovation and enables cross-scale micro-nano damage inspection in advanced manufacturing.