Deep Learning Techniques for Enhanced Underwater Remote Sensing: Applications in Marine Biodiversity and Infrastructure Inspection

Abstract

Underwater remote sensing has become an essential tool for marine biodiversity studies and underwater infrastructure inspection. However, the unique challenges posed by underwater environments, such as light absorption, scattering, and low visibility, necessitate advanced image processing techniques. This research explores the application of deep learning methods tailored specifically for processing and interpreting underwater images and videos. By leveraging convolutional neural networks (CNNs), generative adversarial networks (GANs), and other state-of-the-art deep learning architectures, this study aims to enhance the clarity, accuracy, and interpretability of underwater imagery.

The proposed methods focus on several key areas: improving image quality through noise reduction and color correction, object detection and classification for marine species identification, and anomaly detection for infrastructure inspection. We conducted extensive experiments using diverse underwater datasets to evaluate the performance of these deep-learning models. The results demonstrate significant improvements in image enhancement, accurate identification of marine species, and reliable detection of structural anomalies.

This research provides valuable insights into the integration of deep learning with underwater remote sensing, offering potential advancements in marine biodiversity monitoring and the maintenance of underwater infrastructure. The findings highlight the transformative potential of artificial intelligence in overcoming the limitations of traditional underwater image processing techniques, paving the way for more effective and efficient underwater exploration and conservation efforts.