ORDA2023CAPSTONE Object Detection Dataset and Pre-Trained Model by USCGA

Images

8897 images

Explore Dataset

Here are a few use cases for this project:

Marine Traffic Monitoring: The ORDA2023CAPSTONE model could be used for surveillance and tracking of marine vessels. It can classify the boats, submarines and detect other relevant objects in the vicinity. This could be beneficial for port authorities, coast guard services, and maritime traffic controllers.
Search and Rescue Operations: This computer vision model could be employed during search and rescue operations at sea. It can identify not only various types of vessels but also people, which can assist in locating missing or stranded individuals more effectively.
Environment Monitoring: The model can be used for observing and identifying different forms of marine wildlife and objects contributing to pollution e.g. debris, bottles, etc. in the sea or coastal areas. This application could be helpful for environmental agencies and research institutions.
Water Sports Analysis: ORDA2023CAPSTONE can be used to analyze various water sports events such as sailboat races, surfing, or dinghy competitions. It can identify the type of water vessels, the athletes, and various equipment being used.
Maritime Education and Documentation: The model can be utilized for educational purposes, such as illustrating and explaining different types of naval vessels for students. It may also be handy for documentary creators focusing on marine life, naval technology, or maritime history, allowing them to accurately tag and sort footage.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

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

@misc{
                            orda2023capstone-lgmkh_dataset,
                            title = { ORDA2023CAPSTONE Dataset },
                            type = { Open Source Dataset },
                            author = { USCGA },
                            howpublished = { \url{ https://universe.roboflow.com/uscga/orda2023capstone-lgmkh } },
                            url = { https://universe.roboflow.com/uscga/orda2023capstone-lgmkh },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-04-25 },
                            }