Detecting and Recognizing Marine Animals Using Advanced Deep Learning Models

Accurately detecting and recognizing marine animals is crucial for ecological conservation and underwater monitoring. This research analyzes the application of advanced Deep Learning (DL) techniques, including YOLOV5x6, YoloV3, YoloV8, Faster R-CNN SSD, and RetinaNet, to address the challenges inherent in marine environments. It compares the efficiency of these models in handling diverse underwater scenarios, varying lighting conditions, and identifying a wide range of marine species. It evaluates each DL technique meticulously using a comprehensive image dataset representative of marine ecosystems. Evaluation metrics such as precision, recall, and F1-score are employed to assess the models' abilities to detect and recognize marine animals accurately. The comparative analysis highlights each technique's unique strengths and limitations, focusing on their performance in the intricate task of marine animal identification. Scrutinizes Faster R-CNN's region-based approach, YOLO's grid-based methodology, SSD's single-shot detection, and RetinaNet's focal loss mechanism for their effectiveness in differentiating between species, handling occlusions, and robustly capturing the distinct features of marine fauna. It provides valuable insights into selecting and optimizing DL techniques for marine animal detection and recognition tasks. The outcomes aid practitioners and researchers in making informed decisions based on the specific requirements of their underwater monitoring applications, ultimately advancing the state-of-the-art in marine biology and conservation.