Class	mAP@50	mAP@50-95	Precision	Recall	F1
D00 Longitudinal	0.582	0.319	0.682	0.509	0.583
D10 Transverse	0.597	0.299	0.683	0.515	0.587
D20 Alligator	0.678	0.368	0.715	0.596	0.650
D40 Pothole	0.683	0.349	0.722	0.609	0.661
Overall	0.635	0.334	0.700	0.557	0.621

Full model comparison (same held-out val)

All five models in this study, evaluated identically (imgsz 640):

Model	Variant	Params	Distillation	mAP@50	mAP@50-95	F1
➤ YOLO26n_RDD_Base (this model)	YOLO26n	2.4M	— (GT only)	0.635	0.334	0.621
YOLO26n_RDD_FRDC_Distilled	YOLO26n	2.4M	1 teacher (Co-DETR)	0.640	0.337	0.625
YOLO26n_RDD_FRDC_Distilled_v2	YOLO26n	2.4M	2 teachers (Co-DETR+RTMDet)	0.638	0.337	0.626
YOLO26s_RDD_Base	YOLO26s	9M	— (GT only)	0.687	0.372	0.665
YOLO26s_RDD_FRDC_Distilled_v2	YOLO26s	9M	2 teachers (Co-DETR+RTMDet)	0.692	0.375	0.672

Reading it:

Distillation helps — every distilled model beats its GT-only baseline.
Capacity helps most — the YOLO26s models (+~0.05 mAP@50) clearly outperform the nano ones on this val.
One vs two teachers is a near-tie at nano size; the two-teacher set's edge is small.
Cross-domain check (independent RDDC2024-ID, Indonesia, 8,901 imgs): distilled models generalized better than baselines, while the larger GT-only model generalized worse — evidence the distillation's added data improves robustness, not just in-domain fit.

RDD ground truth has known missing annotations, so absolute precision/recall are conservative for all models. The comparison is fair — every model uses the identical held-out val, never trained on.

Usage

python

from ultralytics import YOLO
model = YOLO("YOLO26n_RDD_Base.pt")
results = model("road.jpg")

Models in this suite

Datasets

Base: TamAko783/Unified_Road_Defect_Dataset
Distillation sets: v1 · v2 (two-teacher)

Credits

Datasets: RDD-2022 (Arya et al.) · UAV-PDD2023 · RoadDamageVision (Silva Zendron & Leithardt, CC BY 4.0).
Distillation teachers: Co-DETR Swin-L + RTMDet-x — FRDC (Wang Fangjun et al.), ORDDC'2024 winner.
Student: YOLO26n, AGPL-3.0 (Ultralytics).

TamAko783/YOLO26n_RDD_Base

YOLO26n · Base (nano, GT-only)

Metrics — RDD held-out validation (4,509 images, 11,470 boxes)

Full model comparison (same held-out val)

Usage

Models in this suite

Datasets

Credits

TamAko783/
YOLO26n_RDD_Base