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
| Hyperparameter | Value |
|---|---|
| Base model | yolo11n-obb.pt (pretrained) |
| Epochs | 100 (early-stopped at 75, patience 20) |
| Image size | 512 × 512 |
| Batch size | 16 |
| Optimizer | AdamW (Ultralytics default) |
| Hardware | Colab Pro T4 GPU |
| Training time | ~2 hours |
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.
| Split | mAP@0.5 | mAP@0.5:0.95 | Precision | Recall | N images |
|---|---|---|---|---|---|
| Overall test | 0.938 | 0.640 | 0.933 | 0.871 | 2,000 |
| Inshore | 0.763 | 0.483 | 0.760 | 0.696 | 159 |
| Offshore | 0.971 | 0.673 | 0.954 | 0.931 | 1,841 |
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
- Inshore/offshore gap: ~21-point mAP gap. Port clutter and coastal infrastructure produce false positives and suppress true detections near shorelines.
- 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. - Single class: No vessel-type classification (tanker, cargo, fishing, etc.).
- Bright-object false positives: Oil platforms, wind turbines, breakwaters, and other high-backscatter structures can produce spurious detections.
- 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.
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