TamAko783/Unified_Road_Defect_FRDC_Pseudolabeled

Unified Road Defect Dataset - FRDC Pseudo-labeled (distillation set)

The exact training set used to train YOLO26n_RDD_FRDC_Distilled following the FRDC (ORDDC'2024 winner) semi-supervised recipe.

Contents

• GT train: the 4-class (D00/D10/D20/D40) Unified Road Defect Dataset (RDD-2022 + UAV-PDD2023 + RoadDamageVision) with original ground-truth labels.
• Pseudo-labeled test: the 9,035 unlabeled RDD-2022 test images, labeled by the Co-DETR (Swin-L) teacher from WangFangjun/FRDC-RDD (conf >= 0.5). 4,719 images with confident detections are included (rddtest_*); the rest were dropped.
• val: the original held-out GT validation split (unchanged).

Layout (Ultralytics YOLO, 4 classes)

data/train_a.tar.gz  data/train_b.tar.gz   # GT train + pseudo-labeled test
data/val.tar.gz                            # held-out GT val
rdd_frdc.yaml                              # Ultralytics data config (nc=4)
samples/                                   # a few pseudo-labeled test images

Files prefixed rddtest_ are the Co-DETR pseudo-labeled RDD-test images.

Classes

0 D00 Longitudinal · 1 D10 Transverse · 2 D20 Alligator · 3 D40 Pothole

Credits

• Base dataset: TamAko783/Unified_Road_Defect_Dataset (RDD-2022 — Arya et al.; UAV-PDD2023; RoadDamageVision — Silva Zendron & Leithardt, CC BY 4.0).
• Pseudo-label teacher: Co-DETR Swin-L, FRDC (Wang Fangjun et al.), Apache-2.0.
• Verify each source's license before redistribution / commercial use.