--- license: other license_name: mixed-source-attribution pretty_name: Unified Road Defect Dataset - FRDC Pseudo-labeled v1+ (Co-DETR, gap-completed) task_categories: - object-detection tags: - road-damage - pothole-detection - crack-detection - yolo - knowledge-distillation - pseudo-labeling - semi-supervised size_categories: - 10K= 0.5, which **dropped 4,316 test images entirely** (no confident detection) and missed lower-confidence boxes. v1+ **keeps every v1 Co-DETR box unchanged** and only **adds the boxes v1 was missing** (non-overlapping detections recovered from Co-DETR on the dropped images + RTMDet), so v1's strong labels are preserved while coverage is completed. Unlike v2 (which fused both teachers with Weighted Boxes Fusion and **averaged/moved** Co-DETR's boxes), v1+ is **add-only** — no existing Co-DETR box is modified. **Contents** - GT train (RDD-2022 + UAV-PDD2023 + RoadDamageVision, 4-class, original labels). - Pseudo-labeled test: **5,081 images, 9,160 boxes** = v1's 8,023 Co-DETR boxes (kept) + 1,137 added missing boxes (362 images v1 had nothing for). - val: the original held-out GT val (unchanged), identical to base/v1/v2 for fair A/B. ## Classes 0 D00 Longitudinal | 1 D10 Transverse | 2 D20 Alligator | 3 D40 Pothole ## Credits - Base dataset: [TamAko783/Unified_Road_Defect_Dataset](https://huggingface.co/datasets/TamAko783/Unified_Road_Defect_Dataset). - Pseudo-label teachers: Co-DETR Swin-L (primary) + RTMDet-x (gap-fill), FRDC (Wang Fangjun et al.), ORDDC'2024 winner.