Underwater Image Recognition using Machine Learning

• Main Authors: Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S
• Format: Article
• Language: English; English
• Published: INTI International University 2024
• Subjects: QA75 Electronic computers. Computer science; T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.intimal.edu.my/2061/1/joit2024_29.pdf; http://eprints.intimal.edu.my/2061/2/602; http://eprints.intimal.edu.my/2061/; http://ipublishing.intimal.edu.my/joint.html

Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. Computers can perform well when it comes to image recognition and classification because of its capacity to detect and record such features as edges, or texture, and shapes among others. A rise in focusing on processing underwater images is essential for various research purposes necessary in marine biology, economy as well as in the management of species’ biodiversity. Observance of such organisms as plankton and Posidonia Oceanic allows determining environmental shifts, global warming, and impact of people on sea creatures. These include respectively planktons that are fundamental for oxygen generation, climatic events and the Posidonia Oceanic, which helps improve the sea Biodiversity and water quality. In the organisation study, image processing supplement the physio-chemical analysis and the sonar detection system. The performances of deep learning models, especially the CNNs, in underwater image processing are significantly better than the conventional methodologies. Preprocessing is important because images are often low-quality; data augmentation and transfer learning tackle the problems of a small dataset and class imbalance, which allow you to save computations during training. Through human activities, marine trash remains a menace to deep sea ecosystems and marine organisms calling for proper debris control.