Small Target Detection in Forward-Looking Sonar Images via LoG5S-LAD Framework

In maritime search and rescue and underwater surveillance missions employing forward-looking sonar, strong reverberation and complex underwater environments often substantially degrade the target signal-to-clutter ratio (SCR), presenting significant challenges for target detection. Existing algorithms typically simplify the point spread function (PSF) into an ideal isotropic model, thereby overlooking the inherent anisotropy induced by its sidelobe structures. This physical model mismatch leads to target energy leakage and severely limits detection performance in complex backgrounds. To overcome the limitations of current target models and detection algorithms, this paper introduces a Gaussian 5 Superposition (G5S) model to accurately characterize the physical features of the PSF and proposes a Laplacian-of-G5S-based Local Adaptive Detection (LoG5S-LAD) method through the construction of a LoG5S filtering operator. Initially, a high-SCR target likelihood map is generated using Hessian-matrix-based geometric gating and LoG5S matched filtering techniques. Subsequently, robust background suppression and the effective preservation of faint targets are achieved through morphological artifact suppression, connected component screening, and a high-energy exemption mechanism. The effectiveness of the proposed framework is validated through model fitting experiments, as well as comprehensive simulations and detection tests across various sonar configurations. Experimental results indicate that the G5S model demonstrates precise fitting capabilities and strong physical adaptability. Furthermore, the proposed LoG5S-LAD algorithm significantly enhances the SCR while maintaining robust detection performance for faint and small-scale targets.