DRF-YOLO: A Transformer-Enhanced Framework for Underwater Image Enhancement and Object Detection

Underwater aquatic systems play a crucial role in maintaining ecological balance and supporting marine biodiversity. However, due to low visibility, color distortion, and scattering effects caused by light absorption, efficient monitoring and detecting objects in such environments remain challenging. Deep learning-based image processing techniques have revolutionized underwater exploration by providing robust solutions for enhancing image quality, extracting meaningful features, and enabling precise classification. Integrating advanced image enhancement methods with deep learning architectures facilitates accurate detection and monitoring of aquatic species, objects, and anomalies. This study introduces a novel approach that synergistically combines the Multiscale Retinex (MSR) and Dark Channel Prior (DCP) approaches for underwater image enhancement in the form of the Dark Retinex Fusion (DRF) model. The DRF model is further integrated with a YOLO-based Transformer framework, leveraging attention mechanisms to enhance feature extraction and classification. The proposed DRF-YOLO-based Transformer framework effectively reduces haze, enhances contrast, and balances colors for an underwater environment. It incorporates advanced spatial precision features in the YOLO backbone and applies the attention module from the Transformer model that captures the long-range dependencies for better contextual understanding. The model was tested on underwater object datasets, achieving an accuracy of 98% and a loss of 0.2, outperforming traditional methods. Additionally, the framework demonstrated resilience to overfitting and local minima, maintaining consistent performance under varying conditions.