Overview of the ImageCLEFcoral 2020 Task: Automated Coral Reef Image Annotation

This paper presents an overview of the ImageCLEFcoral 2020 task that was organised as part of the Conference and Labs of the Evaluation Forum CLEF Labs 2020. The task addresses the problem of automatically segmenting and labelling a collection of underwater images that can be used in combination to create 3D models for the monitoring of coral reefs. The data set comprises 440 human-annotated training images, with 12,082 hand-annotated substrates, from a single geographical region. The test set comprises a further 400 test images, with 8,640 substrates annotated, from four geographical regions ranging in geographical similarity and ecological connectedness to the training data (100 images per subset). 15 teams registered, of which 4 teams submitted 53 runs. The majority of submissions used deep neural networks, generally convolutional ones. Participants’ entries showed that some level of automatically annotating corals and benthic substrates was possible, despite this being a difficult task due to the variation of colour, texture and morphology between and within classification types.