信息与控制2024，Vol.53Issue(4)：520-528,9.DOI:10.13976/j.cnki.xk.2024.3341
一种基于生成对抗网络模型的工件图像数据增广方法
A Method of Augmenting Workpiece Image Data Based on Generative Adversarial Network Model
摘要
Abstract
Generative Adversarial Network(GAN)often demands extensive training data to produce high-quality images.However,the manufacturing industry grapples with a significant shortage of training data for industrial workpieces,presenting challenges in applying conventional GAN models to data augmentation.Consequently,we propose a GAN model capable of training on a small-scale artifact dataset to generate high-quality artifact images,serving as a method to augment the few-sample artifact dataset.Self-attention mechanisms are integrated into both the generator and dis-criminator,incorporating attention mask and attention mapping based on the positions of workpiece voids.This weighted approach enhances the correlation between the regions of workpiece voids and surrounding pixels,thereby mitigating the reliance on extensive training data to a certain extent.A redesigned residual structure is implemented in the generator,employing a combination of upsampling and convolution to ameliorate the"chessboard artifact"phenomenon in generated images,thereby enhancing their realism.The loss function is formulated as a combination of Wasserstein distance and weighted feature matching loss.In comparison to traditional GANs,the proposed model dem-onstrates a reduction in FID score to 100.91 and an elevation in SSIM score to 0.906 for generated workpiece images.After employing the proposed GAN model for data augmentation,the mean Av-erage Precision(mAP)value based on the YOLOv8 defect detection algorithm is elevated to 92.7％.This method can offer a solution to the inadequacy of training samples in industrial inspection.关键词
数据增强/图像生成/生成对抗网络/自注意力机制/缺陷检测Key words
data augmentation/image generation/generative adversarial network/self-attention mechanism/defect detection分类
信息技术与安全科学引用本文复制引用
赵忠文,魏英姿,付垚..一种基于生成对抗网络模型的工件图像数据增广方法[J].信息与控制,2024,53(4):520-528,9.基金项目
辽宁省自然科学基金机器人学国家重点实验室联合开放基金(2022-KF-12-08) （2022-KF-12-08）
