ArcaneEvolution/MScDissertation

MSc Dissertation — Synthetic Underwater Imagery & Sim-to-Real Detection

Supporting data and model checkpoints for an MSc dissertation on synthetic benthic-survey imagery generation and sim-to-real object detection. Benthic scenes are generated with diffusion models (Flux.2-dev, Hunyuan), domain-randomised through a Jerlov water-column physics model, then used to train YOLO detectors evaluated zero-shot against the real DUO dataset.

Full pipeline code, configs, and training scripts: github.com/rithish007/ImgGen_AI

Classes: starfish, sea_urchin, scallop.

Contents

datasets/
  flux2dev/
    base/            1000 images — Flux.2-dev v9 generations, no domain randomization
    placeholder_dr/  2000 images — base set + placeholder-DR copies
    duo_scatter_dr/  2000 images — base set + DUO-calibrated-scatter DR copies (used to train the best model)
    duo_dr/          2000 images — base set + DUO-calibrated DR copies
  hunyuan/            892 images — Hunyuan v8 generations (generated only; not yet trained or evaluated — see note below)

weights/
  flux2dev_base.pt               YOLO trained on datasets/flux2dev/base
  flux2dev_placeholder_dr.pt     YOLO trained on datasets/flux2dev/placeholder_dr
  flux2dev_duo_scatter_dr_BEST.pt  YOLO trained on datasets/flux2dev/duo_scatter_dr — best DUO mAP50 so far
  flux2dev_duo_dr.pt             YOLO trained on datasets/flux2dev/duo_dr

duo_test_detections/  Predictions of flux2dev_duo_scatter_dr_BEST.pt on the real DUO test set
  *.jpg                images with predicted boxes drawn
  labels/*.txt         raw YOLO-format predicted boxes (class x_center y_center width height conf)

Each datasets/flux2dev/* folder is a standard YOLO layout (images/train, images/val, labels/train, labels/val, data.yaml).

Results: zero-shot evaluation on real DUO test set

All four models are trained only on synthetic data and evaluated with no fine-tuning on real imagery.

Variant	Train images	DUO mAP50	DUO mAP50-95
flux2dev_base	900	10.31%	4.78%
flux2dev_placeholder_dr	1800	10.40%	5.21%
flux2dev_duo_dr	1800	11.21%	5.84%
flux2dev_duo_scatter_dr (best)	1800	11.82%	6.18%

Domain randomization calibrated against real DUO water statistics (duo_dr, duo_scatter_dr) consistently outperforms uncalibrated placeholder randomization, which in turn outperforms no randomization at all.

Known limitations

• The Hunyuan dataset was generated but has not yet been used to train or evaluate a model — it's included here for completeness/reproducibility, not as a paired dataset+weights set.
• Scallop and diver false-positive behaviour is under active investigation; see the GitHub repo for the current state of that analysis.