摘要
目的:提升速冻水饺表面缺陷检测的精度。方法:制作了包含五种冻饺形态(正常、露馅、半饺、破肚、粘连)的数据集,提出了用于速冻水饺表面缺陷检测与定位的网络模型GX-RetinaNet。该模型基于RetinaNet网络改进,主干特征提取网络采用ResNeXt-50模型,增强网络特征提取能力,引入卷积块注意力模块(Convolutional Block Attention Module,CBAM)与Swish激活函数有效抑制背景噪声,通过在特征金字塔模块(Feature Pyramid Networks,FPN)后增加PAN结构(Path Aggregation Network)组成双向特征融合模块,可以提升对目标多尺度特征信息的融合能力。结果:GX-RetinaNet网络对工业现场条件下速冻水饺表面缺陷的在线检测精度优于主流的几种目标检测网络,其平均精度均值(mAP)为94.8%,召回率(Recall)为77.0%,F1分数(F1-score)为84.9%。与RetinaNet网络相比,mAP、Recall和F1-score分别提高了2.6%、2.6%、2.4%。结论:GX-RetinaNet网络模型可以满足冻饺表面缺陷检测精度的要求,本研究为深度学习理论在速冻水饺表面缺陷检测方面的应用提供了一种可行的方法。
Objective:To improve the accuracy of surface defect detection of quick-frozen dumplings.Methods:A dataset covering five quick-frozen dumpling forms(normal,leak,half,broken and adhesion)was elaborated,and the GX-RetinaNet network model was proposed for surface defect detection and localization of quick-frozen dumplings.The model was improved based on the RetinaNet network.The backbone feature extraction network adopted the ResNeXt-50 model,which had strong feature extraction ability.The addition of the convolutional block attention module(CBAM)and the use of the Swish activation function could effectively suppress the influence of background noise.By adding the path aggregation network(PAN)structure behind the feature pyramid networks(FPN)structure to form a bidirectional feature fusion module,the fusion ability of target multi-scale feature information could be improved.Results:The online detection accuracy of the GX-RetinaNet network for surface defects of quick-frozen dumplings under industrial field conditions was better than that of several mainstream target detection networks.The mAP was 94.8%,the Recall was 77.0%and the F1-score was 84.9%.Compared with the RetinaNet network,mAP,Recall,and F1-score increased by 2.6%,2.6%and 2.4%,respectively.Conclusion:The GX-RetinaNet network model could meet the requirements of surface defect detection accuracy of quick-frozen dumplings.This study provided a feasible method for the application of deep learning theory in the surface defect detection of frozen dumplings.
作者
费致根
郭兴
宋晓晓
鲁豪
赵鑫昌
FEI Zhigen;GUO Xing;SONG Xiaoxiao;LU Hao;ZHAO Xinchang(Henan Key Laboratory of Intelligent Manufacturing of Machinery and Equipment,School of Mechanical and Electrical Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China)
出处
《食品工业科技》
北大核心
2025年第6期9-19,共11页
Science and Technology of Food Industry
基金
河南省科技攻关项目(222102220050)。
