SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift

2026-07-30

AI Summary

This study addresses critical limitations in existing RGB-based landmine detection datasets, which commonly suffer from annotation errors, imprecise localization, inconsistent class labels, and a lack of robust benchmarks for evaluating model generalization under domain shift. To remedy these issues, the authors systematically correct multi-class annotation flaws in the SULAND dataset, unify out-of-distribution (OOD) category identifiers, and introduce SULAND_v2—a higher-quality, more consistent benchmark that preserves the original images and data splits. Comprehensive evaluations across nine representative detection architectures, including YOLO variants and RF-DETR, are conducted for both in-distribution and out-of-distribution scenarios. Experimental results show that YOLOv12-Small achieves 0.908 mAP@50 in-distribution, while RF-DETR-Large attains 0.799 mAP@50 and 0.675 recall out-of-distribution, substantially enhancing evaluation reliability and establishing a new benchmark for RGB-based landmine detection on UAV/UGV platforms.

Abstract

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.

Problem

• landmine detection
• domain shift
• RGB dataset
• object detection
• benchmark

Innovation

• SULAND_v2
• domain shift
• object detection benchmark
• landmine detection
• annotation refinement