管道(软件)
计算机科学
人工智能
计算机视觉
程序设计语言
作者
Weilun Ren,Kangkang Song,Chin-Yin Chen,Yu Chen,Jingshan Hong,M. Z. Fan,Xiangping Ouyang,Ying Zhu,Jiangjian Xiao
标识
DOI:10.1109/tii.2024.3495786
摘要
High-quality and diverse datasets are crucial for supervised learning in industrial defect detection, yet collecting such data remains challenging. Synthetic data generation offers a promising solution for data augmentation, but traditional methods struggle with controlling key semantic features, leading to domain gaps that impair downstream performance. To address these challenges, we introduce DD-Aug, a novel data augmentation pipeline that integrates Cinema 4-D (C4D) and stable diffusion (SD) to generate photorealistic synthetic images. C4D is used to model key semantic features, such as contours, textures, and lighting conditions, while SD performs style transfer to increase the realism of generated images and reduce the domain gap. DD-Aug translates human visual knowledge into synthetic images, offering precise control over semantic features while adhering to physical rules and reducing the need for complex prompt engineering. This approach enhances generalization, making it particularly suitable for challenging industrial imaging conditions. By balancing semantic consistency and data diversity, DD-Aug significantly improves the quality of synthetic data. Our evaluations on the deformable defect bearing dataset (NBBD) demonstrate that DD-Aug surpasses other generative augmentation methods, yielding a 2.5%–10% improvement in detection accuracy across multiple advanced object detectors compared to models without augmentation. These results demonstrate DD-Aug's potential to significantly advance industrial defect detection through improved synthetic data quality and performance.
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