Annotated videos of luderick from estuaries in southeast Queensland, Australia

Overview

This dataset comprises of annotated footage of Girella tricuspidata in two estuary systems in south East Queensland, Australia. This data is suitable for a range of classification and object detection research in unconstrained underwater environments. The raw data was obtained using submerged action cameras (Haldex Sports Action Cam HD 1080p) to collect video footage in the Tweed River estuary in southeast Queensland (-28.169438, 153.547594), between February and July 2019. Additional footage was collected from seagrass meadows in a separate estuary system in Tallebudgera Creek (-28.109721, 153.448975). Each sampling day, six cameras were deployed for 1 h over a variety of seagrass patches; the angle and placement of cameras was varied among deployment to ensure a variety of backgrounds and fish angles. Videos were trimmed for training to contain only footage of luderick (the target species for the study) and split into 5 frames per second.

Full data report can be found here: https://doi.org/10.3389/fmars.2021.629485

Dataset presentation

This dataset includes 9429 annotations and 4280 images which can be used for training object detection deep learning models and other related computer vision tasks. The dataset is organised into 3 sub-datasets that have been allocated for training, test and novel test purposes.

Dataset	ID	Raw Videos	Version	Suggested use	Luderick Annotations	Bream Annotations	Total
Luderick Seagrass Jack Evans A	Wvo7U_76t	Download (1.3GB)	8	training	6649	53	6702
Luderick Seagrass Jack Evans B	OmKwIVpe-	Download (1.1GB)	8	test	1632	29	1661
Luderick Seagrass Tallebudgera	4bUBoZmvV	Download (576MB)	6	novel test	1023	43	1066
Total				9304	125	9429

Images are included in a ZIP archive which can be downloaded from either of the following:

• https://download.pangaea.de/dataset/926930/files/Fish_automated_identification_and_counting.zip
• https://globalwetlands.blob.core.windows.net/globalwetlands-public/datasets/luderick-seagrass/luderick-seagrass.zip

Each annotation includes object instance annotations which consist of the following key fields: Labels are provided as a common name: either "luderick" for Girella tricuspidata or "bream" for Acanthopagrus australis; Bounding boxes that enclose the species in each frame are provided in "[x, y, width, height]" format, in pixel units; Segmentation masks which outline the species as a polygon are provided as a list of pixel coordinates in the format "[x, y, x, y, ...]"; The corresponding image is provided as an image filename. All image coordinates (bounding box and segmentation masks) are measured from the top left image corner and are 0-indexed.

Annotations are provided in both CSV format and COCO JSON format which is a commonly used data format for integration with object detection frameworks including PyTorch and TensorFlow.

Additional details for each image can be found in dataset_images.csv, including data collection deployment dates, geo-coordinates and habitat type.

COCO JSON

Each annotation in COCO JSON format includes the following fields:

Key	Description
id	INT annotation ID
category_id	INT category ID
image_id	INT image ID
bbox	ARRAY [x, y, width, height] of bounding box in px
area	INT area of bounding box in pixels squared
segmentation	STR segmentation polygon coordinates in format "[[x, y, x, y, ...]]"
iscrowd	INT 0 or 1. A value of 1 indicated the annotation includes more than one individual

Each image in COCO JSON format includes the following fields:

Key	Description
id	INT image ID
height	INT image height (px)
width	INT image width (px)
file_name	STR image filename

Each category in COCO JSON format includes the following fields:

Key	Description
id	INT category ID
name	STR category name (species common name)

COCO JSON Example

{
  "annotations": [{
    "id": 0,
    "image_id": 0,
    "category_id": 1,
    "bbox": [ 0, 76, 624, 1003 ],
    "iscrowd": 0,
    "area": 625872,
    "segmentation": [
      [ 5, 76, 154, 80, 409, 76, 471, 86, 546, 110 ]
    ]
  }],
  "images": [{
      "file_name": "20190618_1.mov_5fps_000001.jpg",
      "height": 1080,
      "width": 1920,
      "id": 0,
      "license": 1
    }
  ],
  "categories": [{
    "name": "luderick",
    "id": 1
  }]

CSV

For each annotation in CSV format, the following columns are provided:

Column	Description
id	INT annotation ID
category	STR name of category (luderick/bream)
category_id	INT category ID
image	STR image file name
image_id	INT image ID
bbox_x	INT minimum x pixel coordinate of bounding box
bbox_y	INT minimum y pixel coordinate of bounding box
bbox_w	INT width of bounding box in pixels
bbox_h	INT height of bounding box in pixels
area	INT area of bounding box in pixels squared
segmentation	STR segmentation polygon coordinates in format "[[x, y, x, y, ...]]"

CSV Example

id	category	category_id	image	image_id	bbox_x	bbox_y	bbox_w	bbox_h	area	segmentation
0	luderick	1	20190618_1.mov_5fps_000001.jpg	0	0	76	624	1003	625872	"[[5, 76, ...]]"