基于改進(jìn)型YOLOv8的木材缺陷檢測及分類
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關(guān)鍵詞：木材缺陷；目標(biāo)檢測；深度學(xué)習(xí)；YOLOv8；特征提取；多尺度融合；算法優(yōu)化；智能識別 中圖分類號：S781.1 文獻(xiàn)標(biāo)識碼：A DOI：10.7525/j.issn.1006-8023.2025.04.011
Abstract：Aimingatthebotteneck problemof insufficientadaptabilityof traditionaldefectdetection methodsinautomated wood processng industry，research onintellgentdetectiontechnologybasedondeep learning iscarriedout，and adatasetcovering multi-species woodcharacteristicsand typicaldefecttypes is proposed.Applyingobjectdetectiontechnology to defect detection，using dilation wise residual（DWR）module to optimize C2f module，and proposing task aligned dynamic detection head （TADDH）and feature focusing spread pyramid network （FSPN） to impove YOLOv8 algorithm（DFT-YOLO）.The experimental results showed that a significant improvement in accuracy，reaching 96.8%， which was 7.9 higher than the original model.On the averageaccuracyof the keyevaluation indicators mAP50 and mAP50-95，the impoved model reached 93. 8% and 75.2% ，respectively，increasing by 6.8% and 17.5% ，respectively.While improving the detection accuracy，the number of parameters of the model had decreased by approximately 1/6 （ 16.2% ）.The impoved model can provide a lightweight detection method for wood defects.
KeyWords：Wood defect；target detection；deep learning;YOLOv8；feature extraction；multi-scalefeature integration; computational optimization；intelligent recognition
0引言
中國作為全球木材消費(fèi)的重要市場，面臨環(huán)保政策日益嚴(yán)格和公眾環(huán)保意識提升的雙重挑戰(zhàn)。（剩余19737字）