融合多层次特征Faster R-CNN的金属板带材表面缺陷检测研究

Study on Surface Defect Detection of Metal Sheet and Strip using Faster R-CNN with Multilevel Feature

针对金属板带材表面缺陷呈现形式存在多样性和随机性而导致难以快速定位并准确识别的问题,提出一种融合多层次特征的Faster R-CNN缺陷目标检测算法(Defect-target detection network,DDN)。该算法采用多层次特征融合网络(Multilevel-feature fusion network,MFN)融合Faster R-CNN中VGG-16提取的各层次特征图,得到具有丰富位置信息和语义信息的融合特征图,后续网络基于该融合特征图产生最终的缺陷检测结果。利用钢带和铜板表面缺陷检测数据集评估本文算法性能,实验结果表明,提出的DNN能够快速准确检测出具有不同尺度的多类缺陷,与Faster R-CNN相比,在不损耗过多检测时间的前提下具有更优的检测精度,平均检测时间为129.65 ms或153.17 ms,平均准确率均值(Mean average precision,mAP)为86.13%或92.54%。

Aiming at the diversity and randomness of the surface defects of the metal sheet and strip,which makes it difficult to quickly locate and accurately identify,a Defect-Target Detection Network(DNN)by using Faster R-CNN with multilevel feature is proposed.This algorithm uses a Multilevel-Feature Fusion Network(MFN)to fuse the feature maps extracted from VGG-16 in Faster R-CNN to obtain fusion feature maps with rich location information and semantic information.The subsequent networks generate the final defect detection results by using the fusion feature maps.The performance of the present algorithm is evaluated by using the surface defect detection data sets of the steel strip and copper sheet.The experimental results show that the present DNN can detect multiple types of defects with different scales quickly and accurately.Comparing with Faster R-CNN,it has better detection accuracy without losing the basic requirement of excessive detection time speed.The average detection time is of 129.65 ms or 153.17 ms and the mean average precision(mAP)is of 86.13%or 92.54%.