A progressive alignment–purification framework for reference-guided variable data printing inspection

Chuangchuang Jiang

doi:10.1007/s10845-026-02985-2

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A progressive alignment–purification framework for reference-guided variable data printing inspection

2026-10-08

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DOI：10.1007/s10845-026-02985-2

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Abstract

En 中文

In Variable Data Printing (VDP) defect detection, legitimate structural variations introduced by dynamic characters and personalized content often cause traditional single-stream detectors to misclassify normal layout changes as defects. Moreover, real industrial scenarios commonly involve nonlinear geometric misalignment and cross-modal discrepancies between photographed images and template images. Under such conditions, direct end-to-end optimization suffers from severe spatial feature interference and optimization instability caused by unreliable cross-modal interactions. To address these issues, we propose Geometric Alignment and Zero-Conv Prior Injection YOLO (GeoZero-YOLO), a reference-guided dual-stream defect detection framework based on Progressive Alignment-Injection Training (PAIT). Specifically, a correlation-guided feature alignment module is introduced to alleviate cross-modal geometric deviations and progressively establish stable structural correspondence priors through an asymmetric optimization strategy. Subsequently, a difference purification mechanism is introduced to suppress pseudo-difference responses and enhance defect-related discrepancy representations. Finally, a progressive prior injection strategy is employed to integrate the refined reference-guided cues while preserving the intrinsic discriminative ability of the original detector. Extensive experiments on a self-built ID Card dataset and the public PCBData dataset demonstrate the effectiveness of the proposed framework for reference-guided VDP defect detection.

Keywords:

Variable data printing

Industrial defect detection

Dual-stream detection

Geometric alignment

Pseudo-difference suppression