COTS and COTS scar dataset

One of the primary challenges in Crown-of-Thorns Starfish (COTS) management is the detection of COTS. Additionally, identifying COTS scars is crucial for locating areas that may be affected by COTS and estimating the probability of their presence. This dataset comprises annotations for COTS and COTS scars derived from images collected by the Australian Institute of Marine Science (AIMS) using their innovative ReefScan platform.\nThe images were thoroughly reviewed by domain experts to identify COTS and COTS scars. For efficiency in annotation, not all images were annotated for COTS scars; however, when an image was annotated for scars, all visible scars within that image were included. For COTS annotations, all visible COTS were annotated.\nThe dataset contains a total of 11,243 images, including annotations for 9,208 COTS and 18,142 scar polygons that can be used to develop an AI model for COTS and COTS scar detection. \nLineage: The data was collected by the Australian Institute of Marine Science (AIMS) using a novel COTS Surveillance System, a towed platform developed by AIMS. This system was designed to meet the needs of the Crown-of-Thorns Starfish (COTS) Control Teams, who are responsible for the majority of starfish control efforts along the Great Barrier Reef (GBR). The platform captures stereo still images at a resolution of 5312x3040 at 4 frames per second (fps).\nSince 2021, AIMS conducted more than six field trips across the Great Barrier Reef to collect data, which was provided to CSIRO for annotation. Domain experts meticulously reviewed the video frames and annotated them using the Computer Vision Annotation Tool (CVAT). CVAT incorporates the Segment Anything Model (SAM), enabling accurate polygon annotations for COTS scars and bounding box annotations for COTS.\nThe annotated dataset is divided into training and test sets. Care was taken to ensure a reasonable number of COTS instances (~61) were included in the test set to facilitate robust evaluation of object detection and tracking algorithms. The training set comprises 8,204 bounding boxes for COTS and 16,321 scar polygons, while the test set contains 1,004 bounding boxes for COTS and 1,821 scar polygons.\nThis annotated dataset will support the development and validation of machine learning models for automated COTS monitoring, contributing to reef conservation efforts.