Underwater Seabed Litter Drone Image Database

Original RAW

These are the original images captured with drones: DJI Mavic pro 2 camera Hasselblad 20MP 4K UHD and DJI air 2S camera 2.4. µm 5.4K UHD.
Key Value
Number of images 5 405
Number of labels 12 786
Size ~193 GB
Format DNG
Resolution 5568 x 3648, 5568 x 3078, 5472 x 3648
Classes 31 (Fragments, Constructional material, Tires, Cans, Bottles, Ropes, Cloth, Bags, Fishing tools, Caps & lids, Cups, Buckets, Pipe, Footwear, Cartons, Dishes, Paper, Chain, Chair, Shopping carts, Trash can, Road sign, Dumpster, Boats, Glove, Car parts, Traffic cone, Flipper, Scooter, Electric drill, Cigarette butt)
Format COCO, Voxel51
Preview No preview available

Half Size

Dataset that has been halved in width and height and converted to PNG for faster manipulation. All other datasets stem from this one.
Key Value
Number of images 5 405
Number of labels 12 786
Size ~33 GB
Format PNG
Resolution 2732 x 1820, 2732 x 1535
Classes 31 (Fragments, Constructional material, Tires, Cans, Bottles, Ropes, Cloth, Bags, Fishing tools, Caps & lids, Cups, Buckets, Pipe, Footwear, Cartons, Dishes, Paper, Chain, Chair, Shopping carts, Trash can, Road sign, Dumpster, Boats, Glove, Car parts, Traffic cone, Flipper, Scooter, Electric drill, Cigarette butt)
Format COCO, Voxel51
Preview Note: Preview is in JPG format to speed up loading

All Patches

Each annotation has been cropped to fit 640x640 size. For larger annotations scaling was used to fit in 640x640. Nearby objects are all included in the same image, but same object might appear in another image). Duplication was kept at minimum by only including something when it's not included already. Some labels might be cropped if they cannot fit in the 640x640.
Key Value
Number of images 6 298
Number of labels 17 913
Size 3.3 GB
Format PNG
Resolution 640 x 640
Classes 31 (Fragments, Constructional material, Tires, Cans, Bottles, Ropes, Cloth, Bags, Fishing tools, Caps & lids, Cups, Buckets, Pipe, Footwear, Cartons, Dishes, Paper, Chain, Chair, Shopping carts, Trash can, Road sign, Dumpster, Boats, Glove, Car parts, Traffic cone, Flipper, Scooter, Electric drill, Cigarette butt)
Format COCO, Voxel51
Preview Note: Preview is in JPG format to speed up loading

Selected Patches

Reduced and curated dataset of patches where only some of the classes are used (that can be properly represented).
Key Value
Number of images 4 016
Number of labels 10 958
Size 2.1 GB
Format PNG
Resolution 640 x 640
Classes 9 (Fragments, Construction material, Tires, Cans, Bottles, Cloth, Bags, Caps & lids, Cups)
Format COCO, YOLO YAML (with train/val/test split)
Preview Note: Preview is in JPG format to speed up loading
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