郑州大学学报（理学版）2026，Vol.58Issue(5)：14-21,8.DOI:10.13705/j.issn.1671-6841.2025068
基于改进YOLO的装甲目标毁伤状态估计方法
Damage State Estimation Method for Armored Targets Based on Improved YOLO
摘要
Abstract
A lightweight armored target damage state evaluation method,the squeeze-and-excitation(SE)and feature fusion enhanced YOLO model,was proposed to address the challenges caused by com-plex battlefield image backgrounds,varying damage levels,and dynamic damage characteristics in target damage state assessment.Firstly,a SE-Focus feature extraction module was designed by adding SE atten-tion mechanism in the Focus module.By enhancing key features and reducing information redundancy,the adaptability and detection performance of the model in complex environments were improved.Second-ly,the advantages of multi-scale feature fusion from the weighted bi-directional feature pyramid network(BiFPN)were integrated with the efficient computing characteristics of depthwise separable convolution(DSConv).While retaining the excellent cross scale feature interaction ability of BiFPN,the feature of DSConv was utilized to reduce model complexity and computational cost.Finally,the SIoU loss function was introduced to enhance the model's robustness and training stability.Experimental results demonstra-ted that when tested on the damaged tank dataset of armored targets,a 1.8 percentage points improve-ment in mAP50,a 1.9 percentage points increase in recall rate,and a 1.7 percentage points rise in mAP50-95 were achieved by the proposed model compared to the original model while the same parameter quantity was maintained,which verified the effectiveness of the proposed improvements.关键词
毁伤检测/YOLO模型/SIoU损失函数/SE注意力机制/双向特征金字塔网络Key words
damage detection/YOLO model/SIoU loss function/SE attention mechanism/BiFPN分类
信息技术与安全科学引用本文复制引用
宋轩,李新家,徐旺旺,佘维..基于改进YOLO的装甲目标毁伤状态估计方法[J].郑州大学学报（理学版）,2026,58(5):14-21,8.基金项目
目标易损性评估全国重点实验室开放基金项目(YSX2024KFPG004) （YSX2024KFPG004）
