Maritime Autonomous Vehicle Monitoring and Response Framework — a simulation-first Python pipeline for multi-sensor maritime detect, track, and classify.

Built by Fratres X AI — reviewable AI, autonomy, and defensive-technology prototypes with physics-first modeling and conservative maturity labels.

On a fresh clone, monitor mode uses simulation detections by default (no YOLO download). YOLO is opt-in via training, --pretrained, or --weights. There is no hosted web demo — clone it, run it, fork it.

Multi-sensor simulation — sonar, acoustic, optical, and magnetic streams

Honest detection modes — simulation default; YOLO when trained or explicitly requested

Fusion & tracking — IoU-matched object IDs, weighted fusion, SORT-style tracks

Operator outputs — bearing, estimated range, bearing rate, contact typing, change detection

File replay — JSON frames via JsonFileSensorAdapter (incoming_data/samples/)

Reproducible gates — seeded benchmark under docs/benchmarks/

Automated tests + GitHub Actions CI

No cloud app. The demo is a local run that writes reports and visualizations under marmf/reports/.

This is the Fratres X open-source posture: you run the stack.

Equivalent one-liner after install:

Regenerate the README GIF from a short run:

From the repo root:

Reports include detection_source (simulation, trained, pretrained, or explicit).

Mode How to enable simulation (default) No flags; uses simulator optical detections trained After main.py --mode train; auto-loads best.pt pretrained --pretrained (downloads yolov8n.pt) explicit --weights PATH

Use --no-trained to skip auto-loading local trained weights.

See docs/ARCHITECTURE.md for pipeline diagram, module map, extension points, and how MAVMRF sits next to other Fratres X defensive-sensing / contested-autonomy work.

Maturity: simulation / prototype — not a fielded maritime C2 product.

Choice Why Tradeoff Simulation detections by default Fresh clone stays honest and offline; no silent COCO YOLO download Optical “detections” are synthetic until you train or pass --weights / --pretrained IoU match before fusion Keeps sonar/acoustic/magnetic joins on the same contact when YOLO boxes differ from sim boxes Assumes overlapping 2D boxes; not a full 3D association layer SORT-style tracker Fast, reviewable track continuity for demos and gates No deep appearance re-ID; coasting behavior is deliberately simple Weighted multi-sensor fusion Makes the multi-modal story concrete in reports Weights are heuristic, not learned calibration from real sensors Clone-and-run only Matches Fratres X “you run it, you own the stack” open-source posture No hosted demo for drive-by clicks

This thread sits alongside Fratres X work on defensive sensing and contested autonomy: multi-modal streams, conservative fusion, and outputs you can audit — without claiming operational readiness.

Real adapter implementations behind SensorAdapter (AIS / acoustic / optical feeds) with recorded replay fixtures

Association beyond 2D IoU (bearing-range gates, timing uncertainty)

Calibration / clutter models grounded in measured sensor noise, not only sim knobs

Stronger track lifecycle metrics (continuity, ID switches) in the seeded benchmark

Optional Docker one-liner for locked environments — still local, still yours

Fixed-seed simulation gate (not field performance):

Results live in docs/benchmarks/ (README.md + seeded_run.json).

MAVMRF is a simulation-first lab baseline. Domain surveys and open trackers that situate the design:

Survey: Multi-sensor data fusion in maritime surveillance (PeerJ) — fusion methods; YOLO + tracking as common optical paths.

Benchmark: Autoferry sensor fusion dataset — multi-sensor MOT fixtures with ground truth.

Validated tracker: harbour-multi-sensor-tracking — radar/camera/AIS/GNSS + EKF/Hungarian, sim + harbour runs.

Full comparison table: docs/RELATED_WORK.md.

Sibling lab threads on fratres-x.com — same posture: physics-first modeling, conservative maturity labels, reviewable prototypes.

Thread Focus Charybdis Contested autonomy — hybrid UUV navigation, conservative fusion, validation gates Anti Edge Node Defensive sensing — acoustic / RF / visual fusion, audit logs, field-node concept HYPERLINK Missile-defense modeling — imperfect model constellations, uncertainty, simulation

Profile pin checklist: docs/PROFILE_SETUP.md.

Doc Description docs/README.md Documentation index docs/ARCHITECTURE.md Pipeline and extension points docs/RELATED_WORK.md Surveys, benchmarks, how MAVMRF fits docs/benchmarks/ Seeded reproducible gates docs/ZENODO_SETUP.md Mint a Zenodo DOI for releases docs/SOLUTION_BRIEF.md Executive narrative docs/capability_matrix.md Capability → evidence docs/DEMO_SCRIPT_3_MIN.md Demo script CHANGELOG.md Release history SUPPORT.md Where to get help SECURITY.md Vulnerability reporting

Full CLI reference: marmf/README.md.

Path Description marmf/ Core framework (package name mavmrf) docs/ Architecture, brief, samples, screenshots, benchmarks CONTRIBUTING.md Setup, tests, CI marmf/tests/ Pytest suite

Python 3.12 · OpenCV · NumPy · Ultralytics (YOLOv8) · PyTorch · Matplotlib · SciPy · pytest · ruff

See CONTRIBUTING.md. By participating, you agree to the Code of Conduct.

See CITATION.cff. Prefer the “Cite this repository” button on GitHub when available.

Portfolio prototype using simulated sensor data. Not intended for operational deployment without live sensor integration and validation. Built for scrutiny — not for inflated claims.

MIT — see LICENSE.