Utilizing Pretrained Models to Enhance Hybrid Neural Networks in Low Data Situations

Abstract Marine ecosystems are vital to the survival of life on Earth, but they are difficult to monitor and comprehend due to their complexity. Evaluating biodiversity and ecological health is hampered by the labor-intensive and scope-restricted nature of traditional observation techniques. We use deep learning and computer vision technologies to analyze underwater imagery taken by remote vehicles in order to overcome these limitations. To accurately identify marine organisms, we systematically evaluate five state-of-the-art neural network architectures: ResNet50V2, ResNet152V2, InceptionV3, Xception, and MobileNetV2. In addition, we suggest a novel hybrid ensemble method that improves detection robustness and accuracy by combining predictions from several models. Our research offers promising new directions for marine management and conservation efforts as it signifies a major breakthrough in automated sea animal detection.