Damage Assessment of Tank Based on Neural Radiance Fields and Hierarchical Analysis

As the modern battlefield environments become increasingly complex, the real-time and accurate assessment of target damage has become a vital prerequisite for improving the combat effectiveness and the commanding and decision-making ability. A tank damage assessment model based on neural radiance field (NeRF) and the analytic hierarchy process (AHP) is proposed for the damage assessment of battlefield targets. The precise segmentation and 3D reconstruction of tank components are achieved by using the segment anything model (SAM) with NeRF. Additionally, a tank damage tree model that incorporates mobility performance (hull, tracks) and attack performance (gun barrel, turret) is constructed, providing a comprehensive representation of functional losses of tanks on the battlefield. The Hungarian algorithm is applied to match the components with damage areas, and the triangular fuzzy AHP is employed to quantitatively analyze the matching results, enabling the accurate damage level assessment. Experimental results demonstrate that the proposed model outperforms the traditional approaches in target segmentation and 3D reconstruction, especially in complex battlefield environments. It breaks through the limitations of traditional 2D-based damage assessment, significantly improving the accuracy and reliability of damage evaluation.