YOLOv11-UAV: An Improved Deep Learning Algorithm for Small Maritime Target Detection

Maritime UAV surveillance requires rapid, accurate identification of small surface targets amidst volatile sea states. Conventional detectors typically degrade under intense wave clutter, variable lighting, and edge computing constraints. To address these limitations, this paper presents YOLOv11-UAV, a compact framework optimized for real-time edge deployment. We introduce an SPPFLSC module integrating large separable kernel attention (LSKA-C) to extend the receptive field with minimal computational overhead. Additionally, an optimized C3k2-EVA block utilizing sparse decomposed large separable kernel attention (SDLSKA) improves feature representation and processing throughput. To refine localization for low-contrast objects, a high-resolution prediction head is integrated into the multi-scale pipeline. Quantitative evaluations on the SeaDronesSee benchmark demonstrate that YOLOv11-UAV yields substantial precision and recall gains, validating its efficacy for airborne maritime reconnaissance.