电子科技2026，Vol.39Issue(5)：54-64,11.DOI:10.16180/j.cnki.issn1007-7820.2026.05.007
改进YOLOv5的多样态木蜂孔洞检测算法
Multi-State Carpenter Bee Holes Detection Algorithm Based on Improved YOLOv5
摘要
Abstract
In view of the problem of identifying the types and shapes of cross-sectional holes in building compo-nents,this study proposes an improved YOLOv5(You Only Look Once version 5)multi-state wood bee hole detec-tion algorithm.The coordinate attention mechanism is integrated in the model backbone network to enhance the fea-ture extraction ability,and the weighted BiFPN(Bi-directional Feature Pyramid Network)is combined to optimize the multi-scale feature integration.The detection head adopts a decoupled structure,separating classification and re-gression tasks,and introduces the loss function EIoU(Efficient Intersection over Union)to enhance the accuracy of target positioning and the convergence efficiency of the model.The experimental results show that the mAP(mean Average Precision)of the improved YOLOv5 model in the wood bee hole detection task is 99.46%.While enhancing the detection accuracy,the proposed method maintains the lightweight of the model,achieving high-precision and high-efficiency detection of wooden honeycomb holes,which is of great value for the assessment and repair of damage grades in wooden buildings.关键词
目标检测/木蜂孔洞/YOLOv5/木结构建筑/建筑保护/CT扫描/坐标注意力机制/解耦头部Key words
target detection/carpenter bee holes/YOLOv5/wood buildings/architectural conservation/CT scan/coordinate attention mechanism/decoupled head分类
信息技术与安全科学引用本文复制引用
张淑化,王宝来,葛浙东,刘国政,房淑宇..改进YOLOv5的多样态木蜂孔洞检测算法[J].电子科技,2026,39(5):54-64,11.基金项目
山东省自然科学基金(ZR2020QC174) （ZR2020QC174）
广西哲学社会科学研究项目(23FMZ025)Natural Science Foundation of Shandong(ZR2020QC174) （23FMZ025）
Philosophy and Social-Science Research Project of Guangxi(23FMZ025) （23FMZ025）
