An antagonistic training algorithm for TFT-LCD module mura defect detection

作者：

Highlights：

• A lightweight backbone network based upon dense blocks with channel attention (DCANet) is proposed and analyzed.

• A data augment method of generate antagonistic sample is presented.

• An antagonistic training framework is proposed to train deep learning-based object detectors.

• Detailed study for the proposed backbone network and training algorithm.

摘要

•A lightweight backbone network based upon dense blocks with channel attention (DCANet) is proposed and analyzed.•A data augment method of generate antagonistic sample is presented.•An antagonistic training framework is proposed to train deep learning-based object detectors.•Detailed study for the proposed backbone network and training algorithm.

论文关键词：Antagonistic training,Antagonistic samples,Mura defect,Classification network

论文评审过程：Received 25 September 2021, Revised 9 April 2022, Accepted 13 June 2022, Available online 17 June 2022, Version of Record 27 June 2022.

论文官网地址：https://doi.org/10.1016/j.image.2022.116791