Enhancing machine vision-based optical module housing defect detection via a diffusion-based data augmentation framework

Abstract In surface quality inspection of optical module housings, deep learning-based automated optical inspection systems often suffer from insufficient training data due to the rarity and long-tailed distribution of defect samples, which introduces significant challenges and limits the reliability of the inspection system. To address this challenge, we propose Inpainting Diffusion-based Data Augmentation (IDDA), a novel data augmentation framework that integrates defect embedding learning and diffusion-based inpainting to enhance the performance and robustness of visual defect detection systems. First, we freeze the UNet and optimize the text encoder to extract defect-specific semantic representations from box-level annotations, creating a calibrated defect descriptor. Then, we introduce a mask-Guided denoising U-Net adaptation, fine-tuning the model to synthesize visually realistic and semantically coherent defects within specified regions of an image, ensuring both spatial and semantic coherence with the context. Experimental results on our dedicated Optical Module Housing Surface Defect (OMHSD) benchmark demonstrate that the defect samples synthesized by our method exhibit superior texture fidelity and global coherence compared to traditional generative approaches. More importantly, by integrating IDDA-generated data, the detection system shows markedly improved detection accuracy and generalization capability, effectively reducing the reliance on manual annotation and offering a pragmatic solution to data scarcity in precision surface inspection.