Roboflow Universe Large Benchmark Datasets AFO - Aerial Dataset of Floating Object

AFO - Aerial Dataset of Floating Object Computer Vision Project

Images

2903 images

Explore Dataset

AFO dataset is the first free dataset for training machine learning and deep learning models for maritime Search and Rescue applications. It contains aerial-drone videos with 40,000 hand-annotated persons and objects floating in the water, many of small size, which makes them difficult to detect.

The AFO dataset contains images taken from fifty video clips containing objects floating on the water surface, captured by the various drone-mounted cameras (from 1280x720 to 3840x2160 resolutions), which have been used to create AFO. From these videos, manually annotated 3647 images that contain 39991 objects were extracted. These have been then split into three parts: the training (67,4% of objects), the test (19,12% of objects), and the validation set (13,48% of objects). In order to prevent overfitting of the model to the given data, the test set contains selected frames from nine videos that were not used in either the training or validation sets.

Cite This Project

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

@misc{
                            afo-aerial-dataset-of-floating-object_dataset,
                            title = { AFO - Aerial Dataset of Floating Object Dataset },
                            type = { Open Source Dataset },
                            author = { Large Benchmark Datasets },
                            howpublished = { \url{ https://universe.roboflow.com/large-benchmark-datasets/afo-aerial-dataset-of-floating-object } },
                            url = { https://universe.roboflow.com/large-benchmark-datasets/afo-aerial-dataset-of-floating-object },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { oct },
                            note = { visited on 2024-04-27 },
                            }