Smart Underwater Monitoring Through Image Enhancement and Object Detection using AI

Abstract - This literature survey examines the rapid advancement of Artificial Intelligence (AI) techniques for underwater image enhancement and object detection. It covers a wide range of approaches, starting from traditional methods such as manual feature extraction and histogram equalization to advanced deep learning models, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformer-based architectures. Recent studies indicate that one-stage detectors, particularly the You Only Look Once (YOLO) family, offer an effective balance between detection speed and accuracy, achieving up to 96–99% mean Average Precision (mAP) in several applications. In addition, GAN-based enhancement techniques combined with attention mechanisms have significantly improved image quality by restoring color and contrast in challenging underwater conditions. Despite these advancements, several challenges remain, including high computational complexity, which limits real-time deployment on resource-constrained Autonomous Underwater Vehicles (AUVs). Moreover, issues such as limited dataset availability, lack of standard evaluation benchmarks, and poor generalization across different underwater environments continue to affect performance. Future research should focus on developing lightweight and explainable AI models to improve efficiency, reliability, and adaptability in real-world underwater applications.