--- license: cc-by-sa-4.0 viewer: false task_categories: - object-detection tags: - sonar - side-scan-sonar - marine-debris - underwater - ghost-net - shipwreck size_categories: - 1K **The preview viewer is off by design.** This is a YOLO-format detection set > (`{split}/images/*.jpg` + `{split}/labels/*.txt`), meant to be pulled with > `snapshot_download` and trained with Ultralytics — it is not a `load_dataset()` > dataset, and HF's auto-parquet converter cannot parse the paired label files. # DRISHTI — side-scan sonar training splits The assembled, preprocessed train / val / test tiles behind the [DRISHTI detector](https://github.com/Rehan9599/Sonar-Drishti) — an SIH 2026 (PS 26057) marine-debris and anomaly detector for side-scan sonar. YOLO format, 640 px tiles. ## Licence — CC-BY-SA-4.0 This release is **Creative Commons Attribution-ShareAlike 4.0 International**. You may share and adapt it, including commercially, provided you (a) **credit** the upstream sources below and this repo, and (b) release any derivative dataset under **CC-BY-SA-4.0** as well. The ShareAlike term is inherited: two of the source datasets (`mine`, and the `crab_pot` set that is *excluded* here) are themselves CC-BY-SA-4.0, so the assembled whole must be too. ### Attribution (required) | Portion | Source | Licence | |---|---|---| | `pipe`, `bg` | [SubPipe / SubPipeMini2](https://zenodo.org/records/10808161) — Álvarez-Tuñón et al., OceanScan-MST | CC-BY-4.0 | | `wreckA` | [AI4Shipwrecks](https://deepblue.lib.umich.edu/data/concern/data_sets/8623hz41x) — Sethuraman et al., UM Field Robotics / NOAA Thunder Bay | CC-BY-4.0 | | `wreckR` | [Side Scan Sonar (Ship, Plane)](https://universe.roboflow.com/dae-hyeok-lee/side-scan-sonar) — Dae Hyeok Lee, Roboflow Universe | CC-BY-4.0 | | `mine` | [Sonar Imaging Mine Detection](https://www.kaggle.com/datasets/sierra022/sonar-imaging-mine-detection) — MILCO contacts | CC-BY-SA-4.0 | | `synth` | procedural acoustic generator (`ml/scripts/build_synthetic_data.py`) | original work, CC-BY-SA-4.0 with the rest | The `synth` background canvases are drawn from the Roboflow SSS set (CC-BY-4.0), then composited with procedurally modelled objects and filtered — the source imagery is not recognisable in the output. ## What is *not* in this release - **`crab_pot`** (class id `0`) — 1,200 tiles were used to train the shipped model as a hard negative, from a HuggingFace set that is now access-gated. They are **omitted here**. Class `0` therefore has zero examples in this repo. To reproduce the exact training set, rebuild from the GitHub `ml/scripts/` recipe. - **KLSG / SeabedObjects (Ship & Airplane)** — never contributed a tile (`KLSG_TRAIN_CAP = 0` in `build_dataset.py`); its weak full-frame boxes hurt shipwreck precision. Listed only so the exclusion is on the record. ## Contents | Split | Images | Labels | |---|---|---| | train | 3,875 | 3,875 | | val | 630 | 630 | | test | 700 | 700 | | **total** | **5,205** | **5,205** | ~1.8 GB. Layout: ``` train/images/*.jpg train/labels/*.txt val/images/*.jpg val/labels/*.txt test/images/*.jpg test/labels/*.txt drishti.yaml # Ultralytics data config ``` Labels are YOLO boxes: `class_id x_center y_center width height` (normalised). ## Classes | id | class | in this release | |---|---|---| | 0 | `crab_pot` | — (excluded, see above) | | 1 | `submarine_pipeline` | ✓ | | 2 | `shipwreck` | ✓ | | 3 | `ghost_net` | ✓ (100 % synthetic) | | 4 | `mine_cylinder` | ✓ | The shipped product uses classes 1–4. `ghost_net` is fully synthetic — no public real ghost-net-in-SSS dataset exists; a Microsoft AI for Good / WWF effort had 412 real segments total and called it a feasibility study. ## Preprocessing — already applied Every tile has been through **Lee speckle filter + CLAHE** (`despeckle_clahe()` in `ml/scripts/preprocess_sonar.py`, clip 3.0, 8×8 grid, 7×7 Lee kernel). Do **not** apply it again. If you train on these tiles, apply the same filter to your inference inputs. Speckle in sonar is *multiplicative* (`I_obs = I_true · n`), so a plain blur destroys the object and shadow edges that carry the signal. The Lee filter is a local MMSE estimate that smooths flat seabed and preserves edges; CLAHE lifts faint contrast with a clip limit so flat-sand speckle is not amplified. Note: an ablation found this preprocessing gave **no accuracy gain** over raw tiles once training-time augmentation was strong — it is retained because CLAHE'd input makes acoustic shadows more detectable for the downstream geometry check. ## Provenance — per prefix (train split) Filenames carry their source. | Prefix | Count | Source | Nature | |---|---|---|---| | `synth` | 1,250 | procedural generator, modelled acoustic physics | original work | | `pipe` | 1,000 | SubPipeMini2 survey strips, tiled | real | | `wreckA`| 546 | AI4Shipwrecks transects (pixel masks → boxes) | real | | `bg` | 500 | object-free SubPipeMini2 tiles (hard negatives) | real | | `wreckR`| 354 | Roboflow side-scan-sonar (Ship + Plane) | real | | `mine` | 225 | Kaggle sonar-mine (MILCO contacts) | real | Full source detail: `docs/PROJECT_RECORD.html` §03 and §15 in the GitHub repo. ## Known caveats - **Shipwreck split not yet audited site-disjoint.** An audit to guarantee no wreck *site* appears in both train and test is pending; treat shipwreck metrics as optimistic until it lands. - **The test set is deliberately hard.** A 50 %-overlap re-tile tripled shipwreck test instances with partial and near-duplicate tiles. Numbers on this split are not comparable to papers using a non-overlapping tiling of the same source. - **`ghost_net` evaluation is synthetic-on-synthetic.** Not a field number. - **Class imbalance is deliberate.** Per-class caps per split; a dominant class suppresses accuracy on feature-dissimilar classes. ## Usage ```python from huggingface_hub import snapshot_download snapshot_download("rehan9599/drishti-sss", repo_type="dataset", local_dir="ml/data/splits") ``` ```bash yolo detect train data=drishti.yaml model=yolov8s.pt imgsz=640 epochs=120 batch=16 ``` ## Citation ```bibtex @software{drishti2026, title = {DRISHTI: AI-Powered Marine Debris Detection from Side-Scan Sonar}, author = {Fazal, Rehan and others}, year = {2026}, note = {Smart India Hackathon 2026, Problem Statement 26057}, url = {https://github.com/Rehan9599/Sonar-Drishti} } ``` Please also cite the upstream datasets listed under **Attribution**.