Military circuit surface defect detection method based on perception autoencoder
This study primarily addresses the task of surface defect detection in military-grade circuits.Given the diversity,small-batch nature,and complex surfaces of these circuits,existing methods demonstrate inadequate performance in image reconstruction.To resolve this,we propose a novel approach based on a Perceptual AutoEncoder(PAE),integrating perceptual loss and self-attention mechanisms into an unsupervised method,thereby enhancing the method's transferability and image reconstruction capabilities.Compared to traditional detection methods,our Perceptual AutoEncoder-based approach eliminates the need for alignment,illumination balance,and color balance,significantly improving transferability across different products and effectively addressing the challenge of detecting various small-batch military circuits.Specifically,we employ feature pyramids and convolutional methods to extract feature vectors of different scales and perform clustering.After clustering,we use the self-attention module to automatically weigh and enhance the features of interest,then reconstruct them into an image,which serves as a template for comparison with the input.Evaluations on our proprietary dataset indicate that the autoencoder,once integrated with perceptual loss,can more accurately detect defects.
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