付思琴,邱涛,王权顺,黄德丰,余华云.基于改进YOLOv4的焊接件表面缺陷检测算法[J].包装工程,2022,43(15):23-32.
FU Si-qin,QIU Tao,WANG Quan-shun,HUANG De-feng,YU Hua-yun.Surface Defect Detection Algorithm of Weldment Based on Improved YOLOv4[J].Packaging Engineering,2022,43(15):23-32. |
| 基于改进YOLOv4的焊接件表面缺陷检测算法 |
| Surface Defect Detection Algorithm of Weldment Based on Improved YOLOv4 |
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| DOI：10.19554/j.cnki.1001-3563.2022.15.003 |
| 中文关键词: 焊接件 缺陷检测 YOLOv4 GhostNet K–means++ |
| 英文关键词: weldment defect detection YOLOv4 GhostNet K-means++ |
| 基金项目:国家自然科学基金(61440023);中国高校产学研创新基金–新一代信息技术创新项目(2020ITA03012) |
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| 中文摘要: |
| 目的 针对真实复杂的工业场景下焊接件表面缺陷检测精度低、速度慢和图像噪声大等问题，提出一种基于卷积神经网络的改进YOLOv4焊接件表面缺陷检测算法。方法 该模型基于YOLOv4算法，首先，考虑到存储和计算资源的限制，使用了轻量级网络GhostNet替换YOLOv4的主干特征提取网络(Backbone)CSPDarknet53;其次，在GhostNet网络结构中嵌入改进的通道注意力机制，能够提高模型的学习能力且减少参数量;最后，引入K–means++聚类算法对焊接件表面缺陷数据集中待检测的标注框宽高进行聚类，使网络模型更容易检测到样本中的缺陷。结果 实验结果表明，改进后的YOLOv4算法平均精度(mean Average Precision，mAP)为91.07%，检测速度达到48.11帧/s，模型尺寸为43.2 MB，比原始YOLOv4算法平均精度提升了4.61%，检测速度提高了26.59帧/s，模型尺寸缩减了82.37%。结论 所提模型提高了焊接件表面缺陷检测的精度和速度，在工业表面缺陷检测中具有现实意义。 |
| 英文摘要: |
| The work aims to propose a surface defect detection algorithm improved based on convolutional neural network, so as to solve the problems of low precision, slow speed and large image noise of weldment surface defect detection in complex industrial scenes. The model was established based on the YOLOv4 algorithm. Firstly, considering the limitation of storage and computational resources, the lightweight network GhostNet was used to replace the YOLOv4 backbone feature extraction network (Backbone) CSPDarknet53. Secondly, an improved channel attention mechanism was embedded in the GhostNet network structure, which improved the learning ability of the model and reduced the parameter quantity. Finally, the K-means++ clustering algorithm was introduced to cluster the width and height of the labeled frames to be detected in the weldment surface defect dataset, so that the network model could detect the defects in the samples. From the experimental results, the improved YOLOv4 algorithm had an average precision (mean Average Precision, mAP) of 91.07%, a detection speed of 48.11 frame/s, and a model size of 43.2 MB. Compared with the original YOLOv4 algorithm, the detection precision was increased by 4.61%, the detection speed was improved by 26.59% frame/s and the model size was reduced by 82.37%. The proposed model improves the detection precision and speed of weldment surface defect, which is of practical significance in industrial surface defect detection. |
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