SULAND v2：考量域偏移之 UAV/UGV 地表地雷偵測的優化 RGB 資料集與深度學習物件偵測基準

摘要

RGB 影像為無人機/無人地面載具（UAV/UGV）在地表地雷偵測中的勘測支援提供了實用且低成本的選項，然而物體偵測器在此安全關鍵領域中仍未被充分探索。跨架構基準測試的有限性以及分布外（OOD）分析之不足，使得偵測器能否在各種部署條件下泛化仍屬未知。此一挑戰因公開RGB地雷資料集的稀缺而更加嚴峻，使SULAND成為PFM-1與PMA-2偵測的關鍵基準。然而，經檢視發現SULAND存在缺失／錯誤標註、定位誤差、能見度標準不一致、視覺偽影、時間序列標註不一致，以及顛倒的OOD類別ID慣例等問題。我們提出SULAND_v2，一個經精煉的RGB地表地雷資料集與基準。在保留原始影像與分割的前提下，我們以人工方式修訂標註，以確保完整性、精確定位、標籤有效性及類別一致性。SULAND_v2包含33,771張影像與12,433個邊界框。我們針對九個架構家族共35種偵測器配置進行基準測試。標註精煉使YOLOv8同分布（IID）測試mAP@50提升14.6至19.6個百分點，而修正OOD類別ID慣例則使YOLOv8平均OOD mAP@50提升約25個百分點。在SULAND_v2上，YOLOv12-Small達到最高IID mAP@50（0.908），而RF-DETR-Large則展現最強的OOD表現（0.799 mAP@50，0.675召回率）。我們的結果證明，高IID準確率並不保證作戰就緒性。SULAND_v2為評估基於RGB之排雷勘測支援中的域移位穩健性，提供了可靠的基準。

English

RGB imagery offers a practical, low-cost option for Unmanned Aerial/Ground Vehicle (UAV/UGV) survey support in surface-landmine detection, but object detectors remain underexplored in this safety-critical domain. Limited cross-architecture benchmarking and insufficient out-of-distribution (OOD) analysis obscure whether detectors generalize across deployment conditions. This challenge is amplified by the scarcity of public RGB landmine datasets, making SULAND a key benchmark for PFM-1 and PMA-2 detection. However, inspection reveals missing/false annotations, localization errors, inconsistent visibility criteria, visual artifacts, temporal labeling inconsistencies, and an inverted OOD class-ID convention in SULAND. We present SULAND_v2, a refined RGB surface-landmine dataset and benchmark. Preserving original images and splits, we manually revise annotations to ensure completeness, precise localization, label validity, and class consistency. SULAND_v2 contains 33,771 images and 12,433 bounding boxes. We benchmark 35 detector configurations across nine families. Annotation refinement improves YOLOv8 in-distribution (IID) test mAP@50 by 14.6-19.6 percentage points, while fixing the OOD class-ID convention increases mean YOLOv8 OOD mAP@50 by ~25 percentage points. On SULAND_v2, YOLOv12-Small achieves the highest IID mAP@50 (0.908), while RF-DETR-Large yields the strongest OOD performance (0.799 mAP@50, 0.675 recall). Our results demonstrate that high IID accuracy does not guarantee operational readiness. SULAND_v2 provides a reliable benchmark for evaluating domain-shift robustness in RGB-based mine-action survey support.