火力与指挥控制2025，Vol.50Issue(8)：47-55,9.DOI:10.3969/j.issn.1002-0640.2025.08.006
面向复杂场景的改进YOLOv8军事目标识别算法
An Improved Yolov8 Algorithm for Military Target Recognition in Complex Scenarios
摘要
Abstract
To address the issues of high missed detection and false detection rates,as well as lower accuracy of military target recognition in complex battlefield environment,an LBI-YOLO military target recognition algorithm with improved YOLOv8 is proposed.The algorithm introduces a large-kernel selective attention mechanism to enhance the feature extraction capability of the backbone network,enabling the model to better focus on important areas;BiFPN is used for multi-scale feature fusion and Inner-IoU loss is use to replace traditional IoU to accelerate model convergence and improve recognition accuracy.Experimental results show that the improved algorithm increase the mAP value of recognition by 5.3%and FPS by 7.4%in the self-built military target dataset.关键词
YOLOv8/大核选择性注意力机制/BiFPN/LBI-YOLO/Inner-IoUKey words
YOLOv8/LSKA/BiFPN/LBI-YOLO/Inner-IoU分类
信息技术与安全科学引用本文复制引用
程国建,沈守婷,白俊卿..面向复杂场景的改进YOLOv8军事目标识别算法[J].火力与指挥控制,2025,50(8):47-55,9.基金项目
陕西省自然科学基金基础研究计划(2023-JC-YB-601) （2023-JC-YB-601）
陕西省计算机学会&翔腾公司基金资助&西安市科技计划高校院所人才服务企业项目(23GXFW0077) （23GXFW0077）
