基于改进YOLOv8的胶合板单板表面缺陷检测

打开文本图片集

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。（剩余13682字）