食品工业科技2025，Vol.46Issue(6)：9-19,11.DOI:10.13386/j.issn1002-0306.2024040041
基于改进RetinaNet模型速冻水饺表面缺陷检测
Surface Defect Detection of Frozen Dumplings Based on Improved RetinaNet Model
摘要
Abstract
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 Fl-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.关键词
速冻水饺表面缺陷检测/RetinaNet/ResNeXt-50/卷积块注意力模块/双向特征融合模块Key words
frozen dumpling surface defect detection/RetinaNet/ResNeXt-50/convolutional block attention module(CBAM)/bidirectional feature fusion module分类
轻工纺织引用本文复制引用
费致根,郭兴,宋晓晓,鲁豪,赵鑫昌..基于改进RetinaNet模型速冻水饺表面缺陷检测[J].食品工业科技,2025,46(6):9-19,11.基金项目
河南省科技攻关项目(222102220050). （222102220050）
