ACCENTECXAI/national-blue-superintelligence

language:

• —en license: cc-by-4.0 task_categories:
• —object-detection task_ids:
• —object-detection prettyname: ACCENTECX AI NATIONAL BLUE SUPERINTELLIGENCE (v1.0) sizecategories:
• —10K<n<100K tags:
• —marine-ai
• —computer-vision
• —object-detection
• —coral-reef
• —fisheries
• —aquaculture
• —maritime
• —underwater ---

<p align="center"> <h1 align="center">🌊 ACCENTECX AI NATIONAL BLUE SUPERINTELLIGENCE (v1.0)</h1> <p align="center"><b>Philippine marine imagery + bounding boxes for ecology, fisheries, aquaculture, maritime awareness, and underwater robotics.</b></p> </p>

<p align="center"> <a href="#-overview">Overview</a> • <a href="#-quick-specs">Quick Specs</a> • <a href="#-classes">Classes</a> • <a href="#-structure">Structure</a> • <a href="#-annotation-protocol">Annotation</a> • <a href="#-uses">Uses</a> • <a href="#-limitations">Limitations</a> • <a href="#-roadmap">Roadmap</a> • <a href="#-citation">Citation</a> </p>

🧭 Overview

The ACCENTECX AI NATIONAL BLUE SUPERINTELLIGENCE (v1.0) is a curated collection of Philippine marine and coastal imagery annotated for object detection using bounding boxes.

It is designed to support:

• —🐟 Fish detection & abundance estimation (proxy)
• —🪸 Coral condition monitoring (healthy vs. bleached)
• —🗑️ Marine litter detection
• —🛥️ Vessel detection & maritime awareness
• —🧰 Aquaculture infrastructure monitoring
• —🤿 Underwater robotics / ROV perception training

Status: Under structured development Planned release: May 2026 Formats: COCO (primary), YOLO (export supported)

⚡ Quick Specs

🏷 Classes

Detection Classes (v1.0)

Design choice (v1.0): grouped ecological classes for stable detection performance. Planned: increased biological granularity in future versions.

🗂 Structure

images/
  train/
  val/
  test/

annotations/
  coco_annotations.json
  yolo_labels/          (optional export)

metadata/
  metadata.csv

Split Strategy

• —70% train / 20% validation / 10% test
• —Splits planned by collection session (not purely random by frame) to reduce near-duplicate leakage.

🌍 Metadata

Each image may include:

• —image_file
• —location
• —site
• —habitat (reef / seagrass / benthic / aquaculture / coastal_surface)
• —depth_min, depth_max (meters, if available)
• —visibility (clear / moderate / turbid, if available)
• —date (if available)
• —source_dive (collection session identifier)

🛠 Annotation Protocol

<details> <summary><b>Click to expand annotation rules (CVAT)</b></summary>

Tool & Type

• —Tool: CVAT
• —Type: Bounding boxes (rectangles)

Core Rules

• —Minimum size: skip objects that are too small / unidentifiable
• —Dense schools: label as fish_school using one grouped box
• —Overlap: annotate separately if visually distinguishable
• —Occlusion: skip heavily occluded objects unless clearly identifiable
• —QA: reviewer validation + consistency checks

</details>

🎯 Uses

<details> <summary><b>Direct Use</b></summary>

• —Marine object detection benchmarking
• —Fish detection + approximate abundance estimation
• —Coral condition monitoring
• —Marine litter detection systems
• —Aquaculture infrastructure monitoring automation
• —ROV/AUV perception training

</details>

<details> <summary><b>Out-of-Scope Use</b></summary>

• —Full species taxonomy modeling (v1.0 uses grouped classes)
• —Precision biomass estimation without calibration/scale instrumentation
• —Identification of individuals (human recognition/biometrics)
• —Safety-critical autonomous navigation without additional validation

</details>

⚠ Limitations

• —Geographic/site distribution may be uneven depending on contributions
• —Water visibility and lighting variability may affect generalization
• —Some classes may be underrepresented (e.g., debris, vessels)
• —Grouped taxonomy reduces biological specificity in v1.0
• —Bounding boxes are coarser than segmentation for coverage/area estimation

🔮 Roadmap

<details> <summary><b>v1.0</b></summary>

• —Core ecological + human-activity detection classes
• —COCO primary export, YOLO optional export
• —Habitat metadata (reef/seagrass/benthic/aquaculture/coastal_surface)

</details>

<details> <summary><b>v2.0 (Planned)</b></summary>

• —Expanded invertebrate subclasses (data-driven)
• —Fish subgrouping (e.g., reeffish vs pelagicfish)
• —Optional instance segmentation for coral/seagrass
• —Expanded regional coverage and benchmarks

</details>

📚 Citation

@dataset{accentecx_open_marine_v1,
  author    = {ACCENTECX AI},
  title     = {ACCENTECX Open Marine Dataset (v1.0)},
  year      = {2026},
  publisher = {Hugging Face}
}

🏢 About ACCENTECX AI

ACCENTECX AI develops marine-focused artificial intelligence systems and structured datasets supporting fisheries monitoring, coral reef analysis, aquaculture analytics, maritime safety, and autonomous marine systems.

Website: https://accentecxai.com