Energy-Aware Floating-Debris Detection for Battery-Powered Electric Unmanned Surface Vehicles: A Lightweight YOLO-Based Method with Embedded Profiling

Battery-powered electric unmanned surface vehicles (e-USVs) and electrified surface-cleaning platforms require reliable onboard vision under strict compute and power constraints. In reflective water environments, tiny floating debris is often obscured by specular highlights, reflection bands, ripples, motion blur, and camera jitter, while label noise further degrades training stability. To improve robustness without increasing onboard inference burden, this paper proposes YOLOv11-IMP, a lightweight detector for reflective water-surface scenes and embedded edge inference. The method integrates a transformer-enhanced backbone stage, a Global Channel–Spatial Attention module in the neck, and a median-enhanced channel–spatial module in the neck to improve global-context modeling, cross-scale interaction, and weak-boundary representation. WIoU-v3 is adopted to improve localization, and a train-time-only noise-aware screening strategy based on the small-loss principle is introduced to suppress unreliable labels without extra inference cost. Experiments on the CAS dataset and a self-built debris dataset show gains of 3.3% in AP@0.75 and 6.5% in AP for small objects over YOLOv11, while maintaining 7.3 GFLOPs and real-time inference on Jetson Nano, demonstrating practical potential for energy-constrained onboard missions.