Search papers, labs, and topics across Lattice.
This paper introduces SULAND_v2, a refined RGB dataset and benchmark for surface-landmine detection using UAVs and UGVs, addressing critical issues in the original SULAND dataset, including annotation errors and inconsistent visibility criteria. The authors manually revised annotations and evaluated 35 object detector configurations, revealing that improved annotation quality significantly enhances in-distribution performance and corrects out-of-distribution class-ID conventions. Key findings show that while YOLOv12-Small achieves the highest in-distribution mAP@50, the best out-of-distribution performance is achieved by RF-DETR-Large, highlighting the disconnect between high IID accuracy and operational readiness.
Annotation refinement boosts YOLOv8's in-distribution mAP@50 by nearly 20 percentage points, while correcting class-ID conventions elevates out-of-distribution performance by about 25 points.
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.
