Effective maritime surveillance and small-scale fisheries management remain challenging in coastal waters, where small vessels are not systematically tracked and are often poorly represented in medium-resolution satellite imagery. Within the AI4COPSEC Horizon Europe framework, this study investigates an object-detection workflow for monitoring small vessels along the Adriatic coasts of Marche and Puglia, Italy. Sentinel-2 and high-resolution PlanetScope RGB imagery were manually annotated to build a task-specific optical dataset and to fine-tune models previously pretrained on a larger SAR-optical vessel dataset. This two-stage strategy was designed to exploit heterogeneous vessel representations during pretraining and then adapt the detector to the target coastal optical domain. The resulting dataset comprised 4,202 image tiles for pretraining and 706 tiles for fine-tuning, with 16,096 and 1,716 vessel annotations, respectively, all belonging to a single target class. Detection experiments were conducted using several YOLOv26 configurations trained under a consistent protocol to assess the trade-off between model complexity, accuracy, and computational efficiency. Among the standard variants, YOLOv26-M achieved the most balanced performance, with Precision of 0.813, Recall of 0.846, F1-score of 0.829, Accuracy of 0.719 and mAP50-95 of 0.306. Pruned and lightweight alternatives showed competitive efficiency-oriented behaviour. Results indicate that, in small-target coastal environments, increasing model size does not necessarily yield proportional gains, whereas task-oriented architectural design improves the balance between detection quality and computational cost.