YOLO11-LREP: A Lightweight Detection Method for Water-Surface Floating Objects on Inland Waterways Under Low-Light and Reflection Interference

Reliable visual detection of small floating objects on the water surface is a prerequisite for environmental monitoring and clean-up tasks performed by unmanned surface vehicles (USVs) on inland waterways. Such scenes are routinely degraded by low illumination at dawn and dusk, strong specular reflections, ripple-induced clutter, and large object-scale variations, which together cause missed detections, false alarms, and unstable localization. This paper proposes YOLO11-LREP, a lightweight detection framework built upon YOLO11n and tailored for water-surface floating-object recognition under such adverse conditions. Four complementary improvements are integrated: (i) a Coordinate Attention (CoordAtt) module is inserted at the top of the backbone to enhance positional encoding and highlight obstacle-related semantic regions; (ii) three Efficient Channel Attention (ECA) modules are embedded at the multi-scale fusion nodes of the Neck so that reflection- and ripple-induced spurious channel responses can be suppressed at almost no extra cost; (iii) the Powerful-IoU (PIoU) loss replaces the original regression loss to enforce four-side boundary alignment and stabilize convergence on small, blurred-edge targets; and (iv) a joint low-light and reflection augmentation strategy, together with CutMix region-level mixing, broadens the training distribution along the illumination and occlusion axes. Experiments on the public FloW-Img dataset, split into 1,200 training and 800 validation images (2,024 instances) and run under a fixed random seed (seed = 0, deterministic = true), show that YOLO11-LREP attains AP₅₀ = 80.1 %, AP₅₀:₉₅ = 38.5 %, and AP_S = 24.3 % with only 2.84 M parameters and 9.3 GFLOPs. On an NVIDIA RTX 4060 Laptop GPU, the model runs at 3.3 ms total per 640×640 image (≈303 FPS), satisfying real-time perception requirements while retaining lightweight deployability. Ablation experiments verify the individual and complementary contributions of each component, and a systematic threshold sensitivity analysis (F₁ fluctuation < 0.2 %) demonstrates the stability of the final model.