关键词：缺陷检测；改进后的YOLOv8模型（CP-YOLOv8）；CA注意力机制；CSPPC模块；WIoUv3；目标检测；轻量化设计中图分类号：S776 文献标识码：A DOI：10.7525/j.issn.1006-8023.2025.04.012

Abstract：Inresponseto thecomplexdiversityofsurface defects inplywood veneersandthe dificulties infeature extraction，as well as the large number of parameters and computational costs of deep learning-based defect detection algorithms，which makes efectiveapplication on devices with lower computing power challenging，a detection model forsurface defects（live knots，dead knots，holes，cracks，and notches）in veneers based onan improvedYOLOv8n is constructed.Toenhance thedetectionaccuracyand lightweightperformanceof the model，improvementsare made to the plywood veneer surface defect detection model.First，anew eficient atention mechanism（coordinate atention，CA）is adopted，which can enhance the acuracyoffeature extraction and the network's spatial information perception ability whileavoiding excesivecomputational burden；secondly，anovel structure basedon partialconvolution（PConv）is proposed- -CSPPC（CSP（coross stage partial） pyramid convolution），it to improve computational efficiency and the fusion capability of multi-scale features； finally，an improved weighted intersection over union loss function- -WIoUv3， it is introduced，which enhances the model'slocalizationaccuracyand robustness.Experimentalresults showthat the improved YOLOv8 model（CP-YOLOv8）performs excellently in the task of detecting surfacedefects in plywood veneers，achieving an average precision mean （mAP）of 93.8% ，an increase of 0.9% over the original model，while reducing the model's floating-point operations （GFLOPs）and parameter count to 7.2Gand2.58 M，respectively，a reductionof O.9 GandO.42 M，which can fully meet practical application needs and provide an eficient，accurate，and lightweight solution for quality inspection of plywood veneers.

0 引言

在全球制造业加速向自动化、智能化深度转型的时代背景下，木材加工行业正经历着深刻变革，而胶合板生产作为其中的关键分支，在建筑、家具和装饰等众多领域发挥着不可或缺的作用。胶合板是一种通过多层木材单板经胶水黏合而成的复合材料，其独特的结构赋予其良好的强度、稳定性和可塑性。目前，单板上的缺陷，如孔洞、裂纹和缺口等，会严重削弱胶合板的强度，影响其外观的美观度和稳定性，进而在使用过程中引发安全隐患或降低产品的使用寿命。在胶合板产业的生产流程中，传统的木材缺陷检测主要采用人工目测[1]。精准且迅速地识别单板表面所存在的各类缺陷（如死节、活节、孔洞、裂缝和缺口等），对于科学划分胶合板等级、提升单板利用率以及保障成品质量具有关键意义。随着人工智能技术的发展，机器视觉逐渐应用到缺陷检测中，Xie等2提出融合表面纹理特征的检测法，挖掘纹理提升检测死节、活节缺陷的准确性与鲁棒性。王金聪等[3运用图像分割技术精准检测死节、活节及虫眼缺陷，克服传统算法分割问题，增强检测效果。然而，需要注意的是，上述这些方法易受木板形状纹理、光照和拍摄角度等环境因素干扰，在复杂背景下检测可靠性与准确性受限，难以满足工业高精度、高稳定性检测需求。

目前，越来越多的研究人员将深度学习用于缺陷检测[4]。……