摘要
目的:对纸质包装盒缺陷进行准确的识别与定位。方法:应用改进Faster R-CNN的网络模型自动对包装盒缺陷进行检测。对训练集图片进行数据增强并添加噪声,提升模型的训练精度和鲁棒性;将特征提取网络替换为ResNet50,并融合特征金字塔网络(FPN),提高模型多尺度检测的能力;使用K-means++对数据集中缺陷尺度进行聚类,优化锚框方案。结果:改进后的Faster R-CNN模型在测试集上的平均准确率(AP)达到93.9%,检测速度达到8.65帧/s。结论:应用改进的Faster R-CNN模型能够有效检测出包装盒缺陷并定位,可应用于包装盒缺陷的自动检测与分拣。
Objective:Accurate identification and location of paper packaging box defects.Methods:The improved network model of Faster R-CNN was applied to automatically detect box defects.The data of the training set picture was enhanced and noise was added to improve the training accuracy and robustness of the model.The feature extraction network was replaced with ResNet50,and the feature pyramid network(FPN)was fused to improve the multi-scale detection ability of the model.K-means++was used to cluster the defect scale in the dataset and optimize the anchor box scheme.Results:The average accuracy(AP)of the improved Faster R-CNN model on the test set reached 93.9%,and the detection speed reached 8.65 f/s.Conclusion:The improved Faster R-CNN model can effectively detect and locate box defects,which can be applied to the automatic detection and sorting of box defects.
作者
夏军勇
王康宇
周宏娣
XIA Junyong;WANG Kangyu;ZHOU Hongdi(School of Mechanical Engineering,Hubei University of Technology,Wuhan,Hubei 430068,China)
出处
《食品与机械》
CSCD
北大核心
2023年第11期131-136,151,共7页
Food and Machinery
基金
湖北省科技创新人才计划(编号:2023DJCO68)。
