Generative AI and Simulation-Based Data Augmentation for Enhanced Object Detection in Low-Data Forestry Environments

Detecting rare ground-level obstacles (e.g., large boulders) in dense boreal forests from low-altitude UAV RGB imagery is challenging due to limited annotated data, strong background clutter, and expensive field labeling. This paper evaluates two complementary synthetic-data augmentation pipelines for low-data forestry object detection: segmentation-guided diffusion inpainting, where SegFormer-derived forest-floor masks constrain Stable Diffusion inpainting to plausible insertion regions, and simulator-based generation in Unreal Engine 5 with controlled domain randomization and automatic annotations. We conduct a ten-fold cross-validation study on a real UAV dataset of 64 images and report both accuracy and stability across folds. Compared to real-only training (mean mAP50 ≈ 0.579; mAP50-95 ≈ 0.350), inpainting improves mean performance (mAP50 ≈ 0.647; mAP50-95 ≈ 0.435) while substantially reducing cross-fold variance and lifting the worst-case fold from 0.301 to 0.619 in mAP50. Simulator augmentation yields slightly lower mean accuracy (mAP50 ≈ 0.546; mAP50-95 ≈ 0.344) but markedly improves robustness by mitigating collapse on difficult splits (minimum mAP50 0.496 vs. 0.301). These results indicate that carefully curated generative augmentation can reduce failure risk and improve generalization in extremely data-limited forestry detection settings without additional field data collection.