Evaluating TFE in YOLO11 P2 Variants for Floating Debris Detection: Performance by Object Size and Computational Cost

This benchmark finds no consistent overall benefit from Tiny Feature Enhancement (TFE) for floating debris detection relative to its added computation. We compare YOLO11 P2 variants with ADown, TFE, TinySCA, and RepC3, alongside other detectors, using complete training annotations and five seeds. The original dataset supports 150 main runs, 75 grouped cross-validation runs, and ten batch-sensitivity runs. On its 40-image test set, scratch-trained P2–ADown reaches 12.15±0.57% AP50–95 versus 10.83±0.98% for YOLO11n, but no overall contrast survives the exploratory Welch/BH correction. Pretrained YOLO11n reaches 12.10%, and the cross-validation advantage of P2–ADown is only 0.19 points. Twenty additional scratch-trained runs on public FloW-Img test four architectures on 800 held-out images. P2–ADown–TFE without TinySCA reaches 34.53±0.62% AP50–95, compared with 33.27±0.70% for P2–ADown and 34.36±0.85% for P2. Thus, TFE helps relative to P2–ADown on FloW, but its mean is close to plain P2. Twenty zero-shot RiSID evaluations remain below 1% AP50–95. These results support a dataset-dependent assessment of accuracy and cost, not a general TFE advantage or readiness for autonomous monitoring.