YOLOv8n-HCDP: Lightweight Wood Defect Detection Model

TANG Jianqiang; XU Zijing; XU Kaihong; CHENG Renxuan; GAO Junzhe

doi:10.12326/j.2096-9694.2025001

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YOLOv8n-HCDP: Lightweight Wood Defect Detection Model
1.College of Computer and Control Engineering，Northeast Forestry University，Harbin 150040，Heilongjiang，China
2.College of Home Furnishing and Art Design，Northeast Forestry University，Harbin 150040，Heilongjiang，China
Vol. 39, Issue 4, Pages: 89-97(2025)
DOI： 10.12326/j.2096-9694.2025001
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唐健强,徐梓敬,徐凯宏等.YOLOv8n-HCDP：轻量化木材缺陷检测模型[J].木材科学与技术,2025,39(04):89-97. DOI： 10.12326/j.2096-9694.2025001.

TANG Jianqiang,XU Zijing,XU Kaihong,et al.YOLOv8n-HCDP: Lightweight Wood Defect Detection Model[J].Chinese Journal of Wood Science and Technology,2025,39(04):89-97. DOI： 10.12326/j.2096-9694.2025001.

摘要

针对木材缺陷检测领域中深度学习模型参数量大，分类检测准确率不高的问题，提出一种基于YOLOv8n的轻量化检测模型YOLOv8n-HCDP。首先，构建轻量化骨干网络HgNetv2（high performance GPU network v2），随后利用动态头（dynamic head）融合轻量级跨尺度特征融合模块（cross-scale feature fusion module，CCFM）得到全新的CCFM-dy模块代替传统的颈部网络和检测头，从而减少模型参数量和计算量；引入动态卷积（dynamic convolution），使网络在保持低计算量的同时从大规模训练中获益；最后，引入创新的PPC结构替换C2f（CSP bottleneck）结构，进一步轻量化模型。结果表明：相较于基准模型，改进模型的参数量减少54.15%，计算量减少44.44%，体积减少51.42%，平均精度均值（mean average precision，mAP）提升2.0%，更适合在硬件资源受限的嵌入式设备上部署。

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

Keywords

references

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