Version 1
: Received: 22 November 2023 / Approved: 23 November 2023 / Online: 23 November 2023 (04:57:34 CET)
Version 2
: Received: 10 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (10:49:55 CET)
Wu, H.; Liu, Y.; Xu, Y. An LCD Detection Method Based on the Simultaneous Automatic Generation of Samples and Masks Using Generative Adversarial Networks. Electronics2023, 12, 5037.
Wu, H.; Liu, Y.; Xu, Y. An LCD Detection Method Based on the Simultaneous Automatic Generation of Samples and Masks Using Generative Adversarial Networks. Electronics 2023, 12, 5037.
Wu, H.; Liu, Y.; Xu, Y. An LCD Detection Method Based on the Simultaneous Automatic Generation of Samples and Masks Using Generative Adversarial Networks. Electronics2023, 12, 5037.
Wu, H.; Liu, Y.; Xu, Y. An LCD Detection Method Based on the Simultaneous Automatic Generation of Samples and Masks Using Generative Adversarial Networks. Electronics 2023, 12, 5037.
Abstract
When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to the imbalance in samples dataset, as well as the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network(GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to generate masks for the defect images automatically. Experimental results shows the effectiveness of our proposed method, as it allows for the simultaneous generation of LCD image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Hao Wu
Commenter's Conflict of Interests: Author