tejassnaikk/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.

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

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

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.

Training procedure

Evaluation results

Evaluated with Ultralytics .val() at IoU threshold 0.5. mAP@0.5:0.95 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 (mAP@0.5: 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.

Limitations

1. 1.Inshore/offshore gap: ~21-point mAP gap. Port clutter and coastal infrastructure produce false positives and suppress true detections near shorelines.
2. 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. 3.Single class: No vessel-type classification (tanker, cargo, fishing, etc.).
4. 4.Bright-object false positives: Oil platforms, wind turbines, breakwaters, and other high-backscatter structures can produce spurious detections.
5. 5.Polarization: Training data is primarily HH-polarized Gaofen-3. Performance on other polarizations (VV, dual-pol) is untested.

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

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).

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.