河南省科技研发项目(212102210078); 河南省重大科技专项(201300210400); 河南省重点研发与推广专项(科技攻关)(202102210380)
针对工业生产中布匹瑕疵自动化检测模型训练时缺少带瑕疵位置信息的瑕疵布匹图像数据集的问题, 本文提出了一种以改进的部分卷积网络作为基本框架的带瑕疵位置信息的瑕疵布匹图像生成模型EC-PConv. 该模型引入小尺寸瑕疵特征提取网络, 将提取出的瑕疵纹理特征与空白mask拼接起来形成带有位置信息和瑕疵纹理特征的mask, 然后以修复方式生成带有瑕疵位置信息的瑕疵布匹图像, 另外, 本文提出一种结合MSE损失的混合损失函数以生成更加清晰的瑕疵纹理. 实验结果表明, 与最新的GAN生成模型相比, 本文提出的生成模型的FID值降低了0.51; 生成的瑕疵布匹图像在布匹瑕疵检测模型中查准率P和MAP值分别提高了0.118和0.106. 实验结果表明, 该方法在瑕疵布匹图像生成上比其他算法更稳定, 能够生成更高质量的带瑕疵位置信息的瑕疵布匹图像, 可较好地解决布匹瑕疵自动化检测模型缺少训练数据集的问题.
Given the problem that no image datasets of defective cloth with defect location information are available for the training of the automatic detection model for cloth defects in industrial production, this study proposes an image generation model EC-PConv with defect location information for defective cloth, and it uses an improved partial convolutional network as its basic framework. This model adopts a feature extraction network for small-scale defects, splices the extracted defect texture features with the blank mask to obtain a mask with position information and defect texture features, and generates an image with defect position information in a repaired way for the defective cloth. Furthermore, a hybrid loss function integrating the mean squared error (MSE) loss is proposed to generate clearer defect textures. The experimental results show that compared with the latest generative adversarial network (GAN) generation model, the proposed model reduces the Frechet inception distance (FID) score by 0.51 and improves the precision P and mean average precision (MAP) values of the generated image of the defective cloth in the cloth defect detection model by 0.118 and 0.106, respectively. This method is more stable than other algorithms in generating images of defective cloth and can generate images of defective cloth that contain defect location information and have higher quality. Therefore, it can effectively solve the problem that no training datasets are available for the automatic detection model for cloth defects.