木材科学与技术2025，Vol.39Issue(4)：89-97,9.DOI:10.12326/j.2096-9694.2025001
YOLOv8n-HCDP:轻量化木材缺陷检测模型
YOLOv8n-HCDP:Lightweight Wood Defect Detection Model
摘要
Abstract
Aiming at the problems of large number of deep learning model parameters and low classification and detection accuracy in the field of wood defect detection,a lightweight detection model YOLOv8N-HCDP based on YOLOv8n was proposed.Firstly,the lightweight backbone network of HgNetv2(high performance GPU network v2)is constructed.Secondly,a new CCFM-dy module is obtained by Dynamic Head fusion with lightweight cross-scale feature fusion module(CCFM)to replace the traditional neck network and detection head,reducing the number of model parameters and calculation amount.Dynamic convolution is introduced to make the network benefit from large-scale training while maintaining low computation.Finally,an innovative PPC structure is introduced to replace CSP bottleneck(C2f)in the network structure to further lightweight the model.The experimental results show that compared with the benchmark model,the improved model has 54.15%less parameters,44.44%less computation,51.42%less volume,and 2.0%more mAP50,which is more suitable for deployment on embedded devices with limited hardware resources.It provides a more efficient defect detection solution for the wood processing industry.关键词
木材缺陷检测/YOLOv8n/轻量化/HgNetv2/平均精度均值Key words
wood defect detection/YOLOv8n/light weight/HgNetv2/mAP分类
农业科技引用本文复制引用
唐健强,徐梓敬,徐凯宏,程仁轩,高俊哲..YOLOv8n-HCDP:轻量化木材缺陷检测模型[J].木材科学与技术,2025,39(4):89-97,9.基金项目
黑龙江重点研发计划资助项目"CRM系统技术开发"(GZ20210018). （GZ20210018）
