LWDS: lightweight DeepSeagrass technique for classifying seagrass from underwater images.

In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous fish, endangered sea cows, Dugong dugon, and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on lightweight DeepSeagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classifies the seagrasses quickly and with lesser parameters. The DeepSeagrass dataset is used to test LWDS's applicability.

Statistical Modeling based Directional Pattern Design (SMDPD) feature extraction for coral reef classification.

Coral reefs are a precious ecosystem that supports majority of marine life. The identification of coral species is essential in the conservation and monitoring process. Distinguishing the coral species among the coral reef family is really a challenging task since they have analogous characteristics and have complex spatial borders between the coral classes. This requires experts to identify corals. But due to inconsistency and biasing of manual labelling, the manual annotations of coral reefs are not feasible. The objective of this research work is to identify various types of corals present in the given input video. This work is aimed at identifying thirty-six types of coral by employing a new feature extraction method called Statistical Modeling based Directional Pattern Design (SMDPD) using a new directional pattern. The proposed work outperforms the state-of-art techniques for four coral datasets namely EILAT, EILAT 2 Red Sea, MLC 2010 and RSMAS. Another advantage of this work is the reduction in feature bin size from 255 to just 16 bins.