EDCS-YOLOv8: A Lightweight YOLOv8n Variant for Ship Detection in Optical Imagery

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Ship detection in shore-based optical imagery must handle both strongly elongated vessel geometry and a severe size-dependent accuracy gap: on the SeaShips benchmark, the median annotation aspect ratio is 4.7, and a YOLOv8n baseline shows a 58-point mAP@50–95 gap between small and large ships, while, in our experiments, common small-object architectural modifications that improve small ships degrade large-ship accuracy. Motivated by these observations, in this paper, we propose EDCS-YOLOv8, a lightweight YOLOv8n variant for ship detection in optical imagery. EIoU replaces CIoU to decouple width and height in the regression of elongated boxes; a dynamic snake convolution lets the P5 backbone sample along elongated hulls instead of a rigid grid; a context anchor attention gates the SPPF output with long-range horizontal context; and a strip-gated block encodes an axis-aligned strip prior in the P3 neck to refine small-ship features. All added modules are near-identity-initialized and warm-started from COCO-pretrained weights, so training starts from the baseline mapping. Experimental results show that, on the SeaShips test set, EDCS-YOLOv8 improves overall mAP@50–95 by 0.66 points (0.48 on average across three random seeds) and medium-ship mAP by 2.00 points, with the gain positive in all three seeds and statistically significant under image-level paired bootstrap over test-set resampling (95% CI [+0.16,+0.77]); a shape-stratified analysis further shows that the improvements concentrate on the more elongated half of instances within every ship category. With 3.46M parameters and 9.0 GFLOPs—an order of magnitude lighter than ship-specific detectors in the literature—the model is suited to cost-sensitive, real-time maritime surveillance.