A Sea Creatures Classification Method using Convolutional Neural Networks

Recently, Autonomous Underwater Vehicles (AUVs) provide high resolution seafloor images to the researchers. The provided images are classified by the researchers one by one, and this task increases the burden on the researchers. In this research, we propose an automatic classification method for the sea creatures using convolutional neural networks to provide the requested information, such as crab images, to researcher. The proposed method is comprised image enhancement process, segmentation process and classification process. In the image enhancement process, which is used Retinex model, the visibility of seafloor images is improved. For the candidate's area selection, the Saliency map is employed to extract the represented areas in seafloor images. In the classification process, the selected areas are recognized based on the biological group. The total candidate detection rate was 64%, and the total recognition accuracy was 67% by the evaluation.