Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (6): 168-172.DOI: 10.3778/j.issn.1002-8331.1712-0040

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Textured Transparent Plastics Crack Detection Based on Improved R-FCN

GUAN Rizhao, CHEN Xindu, WU Lei, XU Zhuoji   

  1. College of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2019-03-15 Published:2019-03-14


关日钊,陈新度,吴  磊,徐焯基   

  1. 广东工业大学 机电工程学院,广州 510006

Abstract: To solve the problem of the detection accuracy and recognition using traditional machine learning method to detect texture with transparent plastic crack rate is not high, an improved detection method based on Region-based Fully Convolutional Networks(R-FCN) is proposed. It makes up for the shortcomings of the original network’s low sensitivity to tiny cracks by using mixed-scale receptive field fusion procession in Residual Network(ResNet). Experimental results show that the crack detection accuracy based on improved R-FCN is about 20% higher than that based on Support Vector Machine(SVM), and is about 8% higher than that based on R-FCN without improvement. The validity of the method is proved.

Key words: crack detection, Support Vector Machine(SVM), Region-based Fully Convolutional Networks(R-FCN), Residual Network(ResNet), receptive field

摘要: 为了解决利用传统的机器学习方法来检测带纹理透明塑料裂痕的检测精度和识别率不高的问题,提出一种改进的基于区域的全卷积网络(Region-based Fully Convolutional Networks,R-FCN)检测方法,通过对R-FCN中的残差网络(Residual Network,ResNet)特征提取网络进行混合尺度感受野融合处理,弥补了原网络对微小裂痕敏感度不高的缺点。实验表明,改进后的R-FCN检测方法的裂痕检测精度比基于传统机器学习支持向量机(Support Vector Machine,SVM)检测方法的裂痕检测准确率高20%左右,比未改进的R-FCN检测方法的检测准确率高8%,证明了该方法的有效性。

关键词: 裂痕检测, 支持向量机(SVM), 基于区域的全卷积网络(R-FCN), 残差网络(ResNet), 感受野