基于改進(jìn)YOLOv8模型的木材缺陷檢測(cè)
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關(guān)鍵詞：木材檢測(cè)；深度學(xué)習(xí)；損失函數(shù)；條件卷積；特征融合；YOLOv8；缺陷識(shí)別 中圖分類(lèi)號(hào)：TP391.41 文獻(xiàn)標(biāo)識(shí)碼：A DOI：10.7525/j.issn.1006-8023.2025.04.010
Abstract：Tosolvetheproblemthatthetargetdetectionalgorithmisprone toleakageandlacksdetectionaccuracy ndetecting wood surface defects，this paper proposes an improved YOLOv8 model（YOLOv8-CBW，C，Band Ware abbreviations for CondSiLU，BiFPNand Wise-IoU）and constructs aself-made dataset containing various wood defects.Byoptimizing theoriginal YOLOv8 algorithmandcombining CondConv（conditional convolution）with SiLU（sigmoidweightedlinearunit）to formtheCondSiLUmodule insteadofthetraditionalconvolutionmodule，theflexibilityoffeature extraction is improved;the bidirectionalfeature pyramid network（BiFPN）is introduced toenhancethe multi-scale feature fusioncapability；andthe Wise-IoU（weighted intersection over union）loss functionreplaces the CIoU（complete intersectionoverunion）to improvetheadaptabilityand generalizationperformanceof the model tolow-qualitysamples. The experimental results show that the improved YOLOv8-CBW model improves the mAP5O（mean average precision at IoU threshold O.5O）and mAP50-95（mean average precision over IoU thresholds from 0.50 to 0.95）by 3.7% and （204號(hào) 3.9% ，respectively，compared with the YOLOv8 model，and it shows higher precision and stability in complex wood defectdetectiontasks.Theresearch in this paper provides new ideasfor wood defectdetection tasksand has good practical application prospects.
Keywords：Wood detection；deep learning；loss function；conditionalconvolution；feature fusion；YOLOv8；defect identification
0 引言
木材作為一種重要的可再生資源，具有重要的生態(tài)價(jià)值1和經(jīng)濟(jì)價(jià)值。（剩余14702字）