Hybrid Deep Architectures for Robust Underwater Object Detection Using Anisotropic Diffusion and Energy Operators

Proposes the hybrid framework for underwater object detection that integrates anisotropic diffusion governed by nonlinear partial differential equations with energy-based regularization operators and deep neural architectures.Rigorous stability and convergence properties of the diffusion process are analyzed, and energy operators are introduced as variational regularizers.Experimental validation on underwater benchmarks demonstrates the benefit of embedding physical priors into deep detection pipelines.