rsdd-yolo11n-obb-v1

YOLOv11n-OBB — RSDD-SAR Ship Detector (v1 baseline)

Rotated bounding-box ship detector trained on RSDD-SAR. Single class: ship. Part of the SAR Dark Ship Detection portfolio project.

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Model overview

• Architecture: YOLOv11n-OBB (nano variant, ~2.7 M parameters)
• Task: Oriented object detection — outputs rotated bounding boxes as 4-corner polygons
• Classes: 1 (ship)
• Input size: 512 × 512 px

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Intended use

This model is intended for:

• Research into SAR-based maritime vessel detection
• Portfolio demonstration of rotated-box detection on remote sensing data
• Baseline for cross-domain evaluation (high-res RSDD-SAR → Sentinel-1 GRD)

Out-of-scope use

• Do not use to accuse individual vessels of illegal activity
• Do not use as sole evidence in any enforcement or legal decision
• Outputs are candidate detections, not verified vessel identifications
• Operational maritime surveillance requires multi-source fusion and human review

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Training data

Dataset: RSDD-SAR (Radar Satellite Dataset for Ship Detection)

• ~7,000 SAR chip images (512 × 512 px, ~3 m ground resolution)
• 10,263 annotated ship instances with rotated bounding boxes
• Annotations in long-edge convention: cx, cy, h, w, angle (radians, h ≥ w)
• Source imagery from Gaofen-3 (C-band, HH/HV polarization)

Split used:

• Train: 5,000 images (from ImageSets/train.txt)
• Val / Test: 2,000 images (from ImageSets/test.txt — no separate val split in dataset; test used for both)
• Test subsets: inshore (159 images), offshore (1,841 images)

Annotations were converted from VOC rotated-box XML to YOLOv11-OBB polygon format using src/data/rsdd_to_yolo.py. Long-edge enforcement was applied to all boxes before conversion.

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Training procedure

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Evaluation results

Evaluated with Ultralytics .val() at IoU threshold 0.5. [email protected]:0.95 is the COCO-style area under the IoU-threshold curve from 0.5 to 0.95 in 0.05 steps.

Key finding: 21-point inshore/offshore gap ([email protected]: 0.763 vs 0.971). The model handles isolated ships in open water well but struggles in port and coastal environments due to clutter, infrastructure returns, and closely spaced vessels.

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Limitations

1. Inshore/offshore gap: ~21-point mAP gap. Port clutter and coastal infrastructure produce false positives and suppress true detections near shorelines.
2. Resolution mismatch: Trained on ~3 m RSDD-SAR; will degrade on Sentinel-1 GRD (~10 m). Cross-domain evaluation is planned for Week 3 of the project.
3. Single class: No vessel-type classification (tanker, cargo, fishing, etc.).
4. Bright-object false positives: Oil platforms, wind turbines, breakwaters, and other high-backscatter structures can produce spurious detections.
5. Polarization: Training data is primarily HH-polarized Gaofen-3. Performance on other polarizations (VV, dual-pol) is untested.

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Ethical considerations

Maritime surveillance is inherently dual-use. This model was built for research; outputs should never be used without human review:

• Detections are AIS-dark vessel candidates, not confirmed illegal ships
• A vessel being absent from AIS does not mean it is acting illegally — AIS failures, fishing vessels under tonnage thresholds, and legitimate AIS-off transit all produce the same signature
• False positive rates at Sentinel-1 resolution (~10 m) are non-trivial and have not been characterized for the cross-domain case
• Any operational use must involve multi-source fusion, domain experts, and legal oversight

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How to use

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Downloads weights (~5.5 MB) and caches locally
weights = hf_hub_download(
    repo_id="tejassnaikk/rsdd-yolo11n-obb-v1",
    filename="best.pt",
)
model = YOLO(weights)

# Run on a SAR image (grayscale or 3-channel, 512×512 recommended)
results = model("path/to/sar_chip.jpg", imgsz=512, conf=0.25)
results[0].show()   # display with rotated boxes

Output format: results[0].obb contains oriented bounding boxes as xywhr tensors (center x, center y, width, height, rotation in radians).

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Citation / Project

@misc{sar-dark-ship-detection,
  author = {Tejas Naik},
  title  = {SAR Dark Ship Detection},
  year   = {2025},
  url    = {https://github.com/tejassnaikk/sar-dark-ship-detection},
}

See the project repo for the full pipeline including annotation conversion, evaluation harness, and Sentinel-1 cross-domain work.