Detection of underwater objects using Deep Learning and You Look Only Once Algorithm
This paper presents a novel approach to underwater object detection using the YOLOV5 algorithm, which leverages the capabilities to efficiently and accurately identify and localize objects in underwater imagery.
Abstract
Underwater object detection plays a pivotal role in various applications ranging from marine biology research to underwater exploration and surveillance. This paper presents a novel approach to underwater object detection using the YOLOV5 algorithm. The proposed underwater object detection model leverages the capabilities to efficiently and accurately identify and localize objects in underwater imagery. We used the aqua pretrain dataset for training and detecting distinct fish species in different water bodies and achieved highest mean average precision value of 0.861 for jelly fish class. This proposed method is helpful in identifying the debris or structures located in the depth of oceans and dealing with underwater calamities.