Integration of Image Processing with Underwater Rover to Monitor Coral Growth.

Abstract

This research outlines the integration of image processing with an Aquabot, an underwater rover to monitor the growth of corals. Designed system composed of underwater vehicle, a floating station for communication and coral monitoring unit. This monitoring unit can classify the coral type and also to identify available diseases on the coral. Communication occurs through a combination of wired and cloud methods. The coral diseasedetection unit was evaluated on a data set of 121 images containing 489 instances of coral disease and the coral varieties detection model was evaluated on a dataset of 124 images containing 492 instances of coral varieties. These images are taken from corals reefs and coral nurseries around Sri Lanka. In both cases the models identified the diseases and varieties correctly with overall confidence levels of 33.1% and 59.6% respectively. The two models achieved box precision of 0.346 and 0.679, the box recallsof 0.331 and 0.558, the mean average precision (mAP50) of 0.295 and 0.596 and the mean average precision at IoUthresholds of 0.5 to 0.95 (mAP50-95) of 0.188 and 0.425 respectively. The results of the evaluations show that both models are effective for respective tasks. This research introduces a combination of AquaBot and an image processing system, which is capable of real time monitoring and identification of corals and its diseases.