Paper Title: DefectGen: Few-Shot Defect Image Generation Using Stable Diffusion for Steel Surface Analysis
Conference Name: The 15th International Conference on ICT Convergence
Abstract: Detecting defects on steel surfaces is crucial for maintaining production standards and minimizing material waste. However, the scarcity of defect images poses a significant challenge in developing robust detection models, which hampers effective defect inspection. To address this issue, we propose DefectGen, a novel data augmentation approach that leverages Stable Diffusion and blending techniques to enhance defect datasets and improve detection performance. With only a limited number of defect images, our method aims to generate a larger and more realistic defect image dataset. The defect image generation process is structured into two stages: the Defect-Free Stage and the Defect Generation Stage. In the Defect-Free Stage, Stable Diffusion is trained on a defect-free dataset to generate realistic steel surface images. The Defect Generation Stage is divided into two phases: defect extraction and modification, followed by blending. In the first phase, defects are extracted from a small number of defect images and modified according to specified conditions. The second phase involves deep blending of the modified defects into the generated defect-free images based on the provided locations, resulting in realistic defect images.Experiments on our steel surface dataset demonstrate the effectiveness of DefectGen, leading to a 11.91\% improvement in Mean Average Precision (mAP) for defect detection.