文章摘要
石敏*,乔昆磊*,王素琴*,朱登明** ***.基于语义分割的密封圈缺陷检测方法研究[J].高技术通讯(中文),2021,31(12):1239~1247
基于语义分割的密封圈缺陷检测方法研究
Research on defect detection method of sealing ring based on semantic segmentation
  
DOI:10.3772/j.issn.1002-0470.2021.12.002
中文关键词: 语义分割; 缺陷检测; U-Net; 密封圈
英文关键词: semantic segmentation, defect detection, U-Net, sealing ring
基金项目:
作者单位
石敏*  
乔昆磊*  
王素琴*  
朱登明** ***  
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中文摘要:
      密封圈的缺陷区域过小、缺陷特征与背景相似度较高和密封圈上杂质过多,导致密封圈表面缺陷检测难度很大。对此,本文提出一种基于编解码结构的语义分割缺陷检测算法。首先,通过向编码器端的各个卷积模块注入不同尺度的图像特征,以此增加模型的多尺度信息和冗余信息,同时使用膨胀卷积层替换常规卷积层,有效地增加网络的感受野,使得模型不易漏掉小的缺陷;其次,结合注意力机制将编码器的信息与解码器端的信息进行级联,加强模型对目标区域信息的捕获;最后,将解码器端输出的不同尺度信息进行融合,提高网络对小缺陷的分割精度。实验结果表明,本文所提出的方法能够对较小的、边缘不清晰的密封圈缺陷进行有效分割,能够满足密封圈缺陷的检测需求。
英文摘要:
      Due to the undersized defect areas of the sealing ring, the high similarity between the defect feature and the background and the redundancy of impurities on the sealing ring, the detection of the sealing ring’s surface defects has been extremely difficult. Aiming at solving the problems faced by defect detection, this paper proposes a semantic segmentation defect detection algorithm based on encoding/decoding structure. Firstly, image features at different scales are inserted into each convolution module on the encoder side to increase the multi-scale information and redundant information of the model. At the same time, the dilated convolution layer is used to replace the conventional convolution layer, therefore effectively increasing the receptive field of the network and decreasing the risk of missing small defects.Secondly, combined with the attention mechanism, the information of the encoder and the decoder is cascaded, and the capture of the target area’s information by this model is optimized. Finally, the fusion of the different-scaled information from the decoder could improve the network’s segmentation accuracy for small defects. The experimental results show that the proposed method can effectively divide the small and unclear seal ring defects, and can meet the requirements of the detection of seal ring defects.
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