For training and testing, they used a set of 18 underwater fish videos that were also recorded with a GoPro underwater camera. In, a multi-cascade object detection network with an ensemble of seven CNN components and two RPNs (Region Proposal Network) linked by sequentially jointly trained LSTMs (Long Short-Term Memory units) was performed. The best results were achieved in classifying 9 of the 20 types of fish that appear most often in the videos. They applied their proposed method to 116 underwater fish videos recorded using a GoPro underwater camera. The study in employed CNN (Convolutional Neural Network) to classify fish by training them with the number of species and their environments, such as reef bottoms and water. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.įor moving fish recognition, many studies have been carried out. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation.
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