摘要
目前水平尺制造行业采用人工方法对水准泡进行出厂检测,其准确率低、速度慢,该文提出一种基于改进Faster R-CNN的水准泡缺陷检测方法。采用ResNet101作为特征提取网络来避免网络退化,同时融合递归特征金字塔(recursive feature pyramid,RFP)得到多尺度的特征图输出,通过主干网络再训练的方式使输出特征图更好地适应模型检测任务。然后针对水准泡数据集样本目标来设计区域生成网络锚框,将得到的多尺度特征图输入区域生成网络进行候选区域提取。最后经过ROI Pooling层后得到水准泡缺陷检测结果。在包含1200张水准泡图像的数据集上进行实验,实验结果表明,融合RFP的Faster R-CNN改进模型能有效提高模型检测准确度,在测试集上的均值平均准确度达96.7%。
Aiming at the problems of low precision and slow speed caused by manual inspection of leveling bubble in the current level ruler manufacturing industry,a defect detection method based on improved Faster R-CNN is proposed in this paper.ResNet101 is used as the feature extraction network to avoid network degradation.At the same time,the Recursive Feature Pyramid is fused to obtain multi-scale feature map output,and the output feature map is better adapted to the model detection task through the retraining of the backbone network.Then,the anchor frame of region generation network is designed for the sample target of level bubble data set,and the obtained multi-scale feature map is input into the region generation network to extract candidate regions.Finally,the level bubble defect detection results are obtained after the ROI Pooling layer.Experiments were performed on a data set containing 1200 level bubble images.The experimental results show that the improved Faster R-CNN model fused with RFP can effectively improve the detection accuracy of the model,and the mean Average Precision on the test set reaches 96.7%.
作者
彭伟康
陈爱军
吴东明
朱利森
PENG Weikang;CHEN Aijun;WU Dongming;ZHU Lisen(College of Metrology and Measurement Engineering,China Jiliang University,Hangzhou 310018,China;Dongjing Group Co.,Ltd.,Jinhua 321015,China)
出处
《中国测试》
CAS
北大核心
2021年第7期6-12,共7页
China Measurement & Test
