Object Detection in Underwater Using Deep Learning Techniques

Abstract

Abstract: Based on the architecture of convolutional neural networks, a model is suggested. The model was created using underwater photography. YOLO is used in this method to locate items underwater. An autonomous underwater item-detecting system is necessary to reduce the cost of underwater inspection. An autonomous underwater item detection system is necessary to reduce the cost of underwater inspection. The main goal of this project is to create a model that can identify and recognize objects. This can be done using deep learning techniques. Object detection has two parts. One is object classification and the other is object localization. Classifying objects into predefined classes classifies objects by location under object localization. Our goal is to test the input images after the system is trained by matching the objects in the training dataset to the training dataset. I suggested using the YOLO model to find objects in images. YOLO is a method that enables real-time object recognition using neural networks. You Only Look Once is known by the acronym YOLO. The precision and speed of this algorithm are what make it so popular. MATLAB will be used to implement YOLO. With MATLAB, there is a deep learning toolset.