Docking_detect Object Detection Dataset and Pre-Trained Model by Aquabot

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Explore Dataset

Here are a few use cases for this project:

Shipping & Logistics Industry: The "Docking_detect" model could be used in the shipping industry to automate the detection and classification of lighting conditions during ship docking. By identifying the relevant light classes, such as coil, the model can efficiently help in maintaining safety standards during nighttime or low-visibility conditions.
Industrial Safety & Operations: In large industrial settings like oil rigs, or manufacturing plants, the model could be used to identify different light sources, including coil lights. This can help improve safety measures by ensuring optimal lighting conditions and preventing accidents due to poor visibility.
Autonomous Navigation Systems: The model could be integrated into autonomous vehicle systems, whether they're cars, drones, or waterborne vessels, where the ability to detect and identify light sources, such as coil lights, could significantly enhance their navigational capabilities, especially in low-light environments or during nighttime conditions.
Smarthome Automation Systems: The "Docking_detect" model could be used in the implementation of smart home automation systems, where it could help in managing and adjusting lighting conditions based on the specific type and source of light detected, including coil lights.
Harbour Management Systems: The model could be incorporated into sophisticated harbour management systems. It can detect and classify different light signals from incoming ships, using the coil class for certain specialized indicators. This would allow for the automation of docking processes and improved operational efficiency at ports.

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{
                            docking_detect_dataset,
                            title = { Docking_detect Dataset },
                            type = { Open Source Dataset },
                            author = { Aquabot },
                            howpublished = { \url{ https://universe.roboflow.com/aquabot-slccc/docking_detect } },
                            url = { https://universe.roboflow.com/aquabot-slccc/docking_detect },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-06-19 },
                            }