Universe

sonar-yolo-2 Computer Vision Dataset

byJovin LeongTask:
Object Detection
License:CC BY 4.0800 views30 downloads
See all 1.9k imagessonar-yolo-2 items-in-the-water samplesonar-yolo-2 items-in-the-water sample showing bodysonar-yolo-2 items-in-the-water sample showing shipsonar-yolo-2 items-in-the-water sample showing bodysonar-yolo-2 items-in-the-water sample showing bodysonar-yolo-2 items-in-the-water sample showing bodysonar-yolo-2 items-in-the-water samplesonar-yolo-2 items-in-the-water sample showing bodysonar-yolo-2 items-in-the-water samplesonar-yolo-2 items-in-the-water samplesonar-yolo-2 items-in-the-water sample showing bodysonar-yolo-2 items-in-the-water sample

About sonar-yolo-2 Dataset

Here are a few use cases for this project:

  1. Maritime Surveillance: The "sonar-yolo-2" can enhance the surveillance capabilities of the coast guard or naval forces, helping them detect illegal activities such as smuggling, fishing, or suspicious movements of bodies, ships, or planes in the water bodies.

  2. Search and Rescue Operations: This solution can assist in locating missing or drowned bodies in bodies of water, or discover wrecked ships or crashed planes, thereby maximizing the efficiency of these critical operations.

  3. Environmental Monitoring: It may prove valuable for tracking the movements of ships to prevent illegal dumping of waste into oceans and seas. It can also be used to verify adherence to maritime traffic regulations.

  4. Aerospace Industry: The aerospace industry may leverage it for investigating plane crashes, where it could help in locating the debris/ parts of the crashed plane underwater, providing valuable inputs to understand the cause of crash.

  5. Harbor Management: The ports and harbors can utilize it for efficient management of the incoming and outgoing ships, maintaining a clear log of ship activities, which could lead to improved safety and operations.

Use Free Ship, Plane and Body Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "ship, plane, body"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ sonar-yolo-2_dataset,
  title = { sonar-yolo-2 Dataset },
  type = { Open Source Dataset },
  author = { Jovin Leong },
  howpublished = { \url{ https://universe.roboflow.com/jovin-leong/sonar-yolo-2 } },
  url = { https://universe.roboflow.com/jovin-leong/sonar-yolo-2 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { jul },
  note = { visited on 2026-07-29 },
}

Similar Projects

See More