Aquatic object detection using YOLO (you only look once) algorithm

The programmed grouping of marine species in view of pictures is a difficult errand for which different arrangements have been progressively given in the beyond twenty years. Seas are complicated environments, hard to get to, and frequently the pictures got are of inferior quality. In such cases, creature arrangement becomes monotonous. Subsequently, it is much of the time important to apply improvement or pre-handling procedures to the pictures, prior to applying grouping calculations. The goal is to develop a deep learning system that is both extremely accurate and efficient, utilizing the YOLOv8 (You Only Look Once) algorithm to recognize a variety of aquatic living species underwater. Consequently, we proposed a submerged optical discovery organization (UODN) in light of the YOLO algorithm. The findings not only affirm the suitability of YOLOv8 for underwater exploration but also highlight its potential strength in diverse fields, such as marine resource identification, rescue operations and ecosystem preservation. The intersection of deep learning and underwater environments opens new avenues for technological advancements with far-reaching implications for both scientific research and practical applications.