SOD Computer Vision Project

LaMAO

Updated 8 months ago

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Description

Sidewalk Obstacle Detection (SOD) is a custom image dataset designed for training and validating machine learning models to accurately identify 22 common sidewalk obstacles that could hinder the mobility of visually impaired individuals in outdoor spaces. This extensive dataset comprises 10,000 images, 30,834 annotated instances, and 22 distinct classes: person, car, tree, spherical_roadblock, warning_column, waste_container, street_light, fire_hydrant, traffic_light, stop_sign, pole, bench, curb, stairs, bicycle, motorcycle, dog, bus, truck, train, bus_stop, and cane.

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LICENSE

CC BY 4.0

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

                        @misc{
                            sod-enect_dataset,
                            title = { SOD Dataset },
                            type = { Open Source Dataset },
                            author = { LaMAO },
                            howpublished = { \url{ https://universe.roboflow.com/lamao/sod-enect } },
                            url = { https://universe.roboflow.com/lamao/sod-enect },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-12-22 },
                            }