CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection

作者：

Highlights：

• Cosmetic defect detection is an essential process in the PCB industry.

• We propose a cost-sensitive ResNet (CS-ResNet) for PCB cosmetic defect detection.

• In CS-ResNet, we add a cost-sensitive adjustment layer to optimize the CS-ResNet.

• Extensive empirical studies validate the effectiveness of the proposed CS-ResNet.

摘要

•Cosmetic defect detection is an essential process in the PCB industry.•We propose a cost-sensitive ResNet (CS-ResNet) for PCB cosmetic defect detection.•In CS-ResNet, we add a cost-sensitive adjustment layer to optimize the CS-ResNet.•Extensive empirical studies validate the effectiveness of the proposed CS-ResNet.

论文关键词：PCB cosmetic defect detection,Residual convolutional neural network,Class imbalance,Cost-sensitive learning

论文评审过程：Received 18 February 2021, Revised 20 May 2021, Accepted 24 July 2021, Available online 29 July 2021, Version of Record 2 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115673