森林工程2025，Vol.41Issue(4)：750-760,11.DOI:10.7525/j.issn.1006-8023.2025.04.010
基于改进YOLOv8模型的木材缺陷检测
Research on Wood Defect Detection Based on Improved YOLOv8 Model
摘要
Abstract
To solve the problem that the target detection algorithm is prone to leakage and lacks detection accuracy in de-tecting wood surface defects,this paper proposes an improved YOLOv8 model(YOLOv8-CBW,C,B and W are abbre-viations for CondSiLU,BiFPN and Wise-IoU)and constructs a self-made dataset containing various wood defects.By op-timizing the original YOLOv8 algorithm and combining CondConv(conditional convolution)with SiLU(sigmoid-weighted linear unit)to form the CondSiLU module instead of the traditional convolution module,the flexibility of feature extraction is improved;the bidirectional feature pyramid network(BiFPN)is introduced to enhance the multi-scale fea-ture fusion capability;and the Wise-IoU(weighted intersection over union)loss function replaces the CIoU(complete intersection over union)to improve the adaptability and generalization performance of the model to low-quality samples.The experimental results show that the improved YOLOv8-CBW model improves the mAP50(mean average precision at IoU threshold 0.50)and mAP50-95(mean average precision over IoU thresholds from 0.50 to 0.95)by 3.7%and 3.9%,respectively,compared with the YOLOv8 model,and it shows higher precision and stability in complex wood de-fect detection tasks.The research in this paper provides new ideas for wood defect detection tasks and has good practical application prospects.关键词
木材检测/深度学习/损失函数/条件卷积/特征融合/YOLOv8/缺陷识别Key words
Wood detection/deep learning/loss function/conditional convolution/feature fusion/YOLOv8/defect identification分类
信息技术与安全科学引用本文复制引用
颜世运,张慧斌,籍浩恺,丁禹程,白岩,杨春梅..基于改进YOLOv8模型的木材缺陷检测[J].森林工程,2025,41(4):750-760,11.基金项目
黑龙江省重大成果转化项目(CG23013) （CG23013）
黑龙江省"双一流"学科协同创新成果项目(LJGXCG2024-F16). （LJGXCG2024-F16）
