Industrial Defect Detection Algorithm Based on Diffusion-Variational Autoencoder
Reconstruction-based detection algorithms are extensively employed for automatic defect detection in industrial products.Nevertheless,they frequently struggle to effectively eliminate defect features from reconstructed images,resulting in diminished detection accuracy.This paper proposes an industrial product defect detection algorithm based on a diffusion-variational autoencoder to address this issue.The algorithm treats defects as noise and reconstructs typical images through the reverse denoising process of a diffusion model.During training,a pre-trained vector quantized variational autoencoder(VQ-VAE)is utilized to extract normal features and add noise to industrial product images.The diffusion model is then employed to eliminate defect features while preserving normal features during denoising,resulting in reconstructed normal images.Defects can be determined and localized by comparing the reconstructed images with original image.In the testing phase,input images are treated as noise-added for defect detection.Experimental results demonstrate a significant improvement in detection accuracy compared to other algorithms.
computer visiondefect detectionvector quantized variational autoencoder(VQ-VAE)diffusion model
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