Coral Reef Annotation and Localization using Faster R-CNN

Coral reefs are the most diverse and valuable ecosystems in the world. It is also called as rainforests of the sea as they are so diverse. Coral reefs are important since it provide shelter and food to many marine species and also act as the source of nitrogen and other essential nutrients for marine food chains. Recent studies show that coral reefs ecosystems are extremely threatened due to pollution, sedimentation, unviable fishing practices, and climate change. So, coral reefs should be protected and monitored to save marine ecosystem. Hence, to monitor coral reef a task was introduced in ImageCLEF 2019, to automatically identify and label different types of benthic substrate with bounding boxes in a given image. This paper presents a Convolutional Neural Network (CNN) based method to locate and detect different types of benthic substrate. We have used faster RCNN architecture to detect the substrate since this method is much faster and accurate in detecting the objects.