1.北京建筑材料科学研究总院有限公司，北京 100041
2.北京金隅集团股份有限公司，北京 100010
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王文财,党亚光,朱翔等.基于Lite-YOLOv5s模型的刨花板表面缺陷检测方法[J].木材科学与技术,2023,37(03):58-67.
WANG Wencai,DANG Yaguang,ZHU Xiang,et al.Detecting Method for Particleboard Surface Defects Based on the Lite-YOLOv5s Model[J].Chinese Journal of Wood Science and Technology,2023,37(03):58-67.
针对目前刨花板表面缺陷检测两阶段方法计算量大、机器学习识别方法鲁棒性低等问题，提出一种基于改进YOLOv5s的轻量化检测模型，即Lite-YOLOv5s。首先，在主干网络和颈部网络中引入Ghost Bottleneck模块以减少模型参数量，降低模型计算成本。其次，在主干网络中增加坐标注意力机制（coordinate attention，CA），并将空间金字塔池化模块（spatial pyramid pooling-fast，SPPF）替换为简化空间金字塔池化模块（simplify spatial pyramid pooling-fast，SimSPPF），保证模型在降低计算量的同时仍具有较好的检测效果。最后，在替换颈部网络中使用深度卷积模块（depth wise convolution，DWConv），进一步优化模型运行成本。应用Lite-YOLOv5s模型对某工厂刨花板四种表面缺陷数据集进行模型训练和验证，并将训练的模型用于刨花板图像的缺陷检测，结果表明：Lite-YOLOv5s模型针对刨花、胶斑、油污及粉尘斑四种缺陷的平均检测精度（mean average precious，mAP）可达90%以上，针对样本数量较少的漏芯缺陷mAP为75%以上；与原YOLOv5s模型相比，模型训练时间减少约3.58%，模型参数量下降约63.5%，模型权重文件大小下降约60.54%，模型浮点计算量下降约65.2%，在保证检测精度的前提下有效降低了模型运行成本，使其更容易部署在资源有限的边缘侧设备中。
In order to improve the current two-stage method for particleboard surface defects detection, including a large amount of computation and a low robustness of the machine learning recognition, this paper proposes a lightweight detection method based on modified YOLOv5s, namely Lite-YOLOv5s. First, the Ghost Bottleneck module is introduced into the backbone and neck networks to lower the model parameters and reduce the model calculation cost. Secondly, the Coordinate Attention (CA) mechanism is added to the backbone network, while the Spatial Pyramid Pooling-Fast (SPPF) module is replaced by the Simplify Spatial Pyramid Pooling-Fast (SimSPPF) module to ensure that the model can still have a good detection effect after reducing the amount of computation. Finally, in replacing the neck network, a Deep Wise Convolution module (DWConv) is used to further optimize the model operation cost. The Lite-YOLOv5s model was applied to model training and validation on four surface defect data sets of particleboards in a factory. Then the trained model was applied to defect detection of particleboard images. The results showed that the Lite-YOLOv5s model had more than 90% mAP for the four defects of shavings, glue spots, oil stains, and dust spots, and more than 75% mean average precious (mAP) for the core leakage defect with a small number of samples. Compared with the original YOLOv5s model, the model training time is reduced by about 3.58%, the model parameter amount is reduced by about 63.5%, the model weight file size is reduced by about 60.54%, and the model floating point calculation amount is reduced by about 65.2%. While the detection accuracy is maintained, the model running cost is effectively reduced as well as facilitating deployment in edge devices with limited resources.
刨花板表面缺陷检测图像Lite-YOLOv5s轻量化深度学习
particleboardsurface defect detectionimageLite-YOLOv5slightweightdeep learning
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