GLASS-FFT: A fast and accurate framework for micro-scale defect detection in linear guideway end seals

Abstract Micro-scale linear guideways increasingly rely on face-seal gaskets whose defects are minute and low-contrast; such defects must be inspected within 20 s, a requirement that increases miss rates. The high cost of pixel-level labels limits the practicality of fully supervised convolutional neural network (CNN)-based approaches. To address these constraints, we propose GLASS-FFT-SA, a framework that fuses a dual-layer anomaly synthesis strategy with frequency domain convolution and a lightweight spectralattention (SA) block that selectively amplifies highfrequency defect cues. Local Anomaly Synthesis inserts defect textures into normal images to create strong anomalies, whereas global anomaly synthesis performs gradient ascent on the feature manifold to generate subtle, near-boundary anomalies; together, they furnish abundant, diverse training data. Replacing the large 7 × 7 and 5 × 5 kernels in a ResNet-34 backbone with FFT-based convolutions reduces the computational complexity of those layers and reduces inference latency by approximately 30%, enabling near-real-time operation. To avoid running the pixel head on every frame, we introduce a gated crossattention mechanism (GCAM) that activates the pixel branch only when the image head’s anomaly score ŷ exceeds a learnable hardsigmoid gate. Trained on 10,000 normal images and 8,000 synthetically generated anomalies, GLASS-FFT-SA achieved an image-level AUROC of 0.99, a pixel-level AUROC of 0.97, an AUPRO of 0.95, and a throughput of 38 FPS on RTX 3090—matching the precision of original GLASS while operating at approximately 40% higher speed. It also sustains AUROCs above 0.93 across variations in product type, illumination, and resolution, outperforming conventional CNNs, ResNet-34-FFT-SA, and PatchCore. These results suggest that combining spectrum-efficient convolutions with tailored anomaly synthesis can deliver both accuracy and throughput for fine-grained defect inspection.