关键词: 坦克, 毁伤评估, 三维掩码, 神经辐射场, 匈牙利算法, 层次分析法

Abstract: As modern battlefield environments become increasingly complex, real-time and accurate assessment of target damage has become a vital prerequisite for enhancing combat effectiveness and informed command decisions. To address the challenge of damage assessment for battlefield targets, a tank damage assessment model based on Neural Radiance Field (NeRF) and the Analytic Hierarchy Process (AHP) is proposed. By integrating the Segment Anything Model (SAM) with NeRF, precise segmentation and 3D reconstruction of tank components are achieved. Additionally, a tank damage tree model is constructed, incorporating mobility performance (hull, tracks) and attack performance (gun barrel, turret), providing a comprehensive representation of functional losses of tanks on the battlefield. The Hungarian algorithm is applied to match components with damage areas, and the triangular fuzzy AHP is employed to quantify the matching results, enabling accurate damage level assessment. Experimental results demonstrate that this method outperforms 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.

Key words: tank, damage assessment, 3D mask, neural radiance fields, hungarian algorithm, analytic hierarchy process