| --- |
| 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<n<10K |
| --- |
| |
| > **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**. |
| |
