<p>Detailed parameters of the training model.</p>

However, due to challenges such as small ship targets, complex marine backgrounds, and speckle noise interference, existing methods still suffer from insufficient accuracy in small-target detection scenarios. To address this issue, this paper proposes a high-resolution detection algorithm named LHR-YOLO, based on YOLO11.

In the Stem stage, a Gaussian–Laplacian edge-enhancement and Gaussian-filtering mechanism is introduced to effectively improve shallow-feature perception for tiny ship targets. In the Backbone, an improved C3k2 module is designed by incorporating channel shuffling, re-parameterized convolutions, and feature aggregation strategies to enhance multi-scale feature representation. Meanwhile, in the Neck–Detect stage, high-frequency perception, spatial dependency modeling, and a high-resolution detection head are integrated to further improve the localization accuracy of extremely small targets.