Analysis of Oplegnathus Punctatus Body Parameters Using Underwater Stereo Vision

Due to the loss of underwater light resources, monitoring fish situations in underwater surveillance has led to fish distortion. Monitoring and observing fish situations in culture ponds around the world is becoming more and more important to prevent them from suffering uncertain damage. In this study, we propose a stereo underwater surveillance system for Oplegnathus punctatus by developing an underwater depth prediction model and an underwater fish skeleton model based on deep learning. The underwater depth prediction model is a convolutional neural network-based method for extracting underwater depth spatial features. The fish skeleton prediction model is for extracting 9 keypoints on the fish body. Additionally, since there is no established underwater Oplegnathus punctatus dataset for fish body analysis in culture ponds, we have collected and proposed a depth and skeleton Oplegnathus punctatus dataset, which contains underwater information on Oplegnathus punctatus bodies. The experimental results on our self-collected dataset show that the fish body information measurement achieves 94% accuracy in weight. We also compared our proposed method with Mask-RCNN and stereo-matching methods, and our method proved to be the most effective.