TaDP-Det: Semi-Supervised Texture-Aware Dynamic Pseudo-Labeling Detector for Industrial Surface Defect Detection

Surface defect detection is essential for industrial quality control, but obtaining reliable labeled data remains costly due to the need for expert annotation. Semi-supervised object detection (SSOD) mitigates this need by leveraging unlabeled data through pseudo-labeling. However, industrial surface imagery presents specific challenges, including texture-ambiguous, low-contrast backgrounds that cause foreground-background confusion and strong class-dependent detection difficulty, which renders global confidence thresholds ineffective, often yielding noisy and imbalanced pseudo labels. To overcome these limitations, we propose TaDP-Det, a semi-supervised detector that improves pseudo-label quality through dual enhancements in feature representation and label filtering. We first introduce a Texture Enhance Module (TEM), designed as a texture-aware patch-level mixture-of-experts applied at shallow backbone stages, which amplifies discriminative low-level texture cues to generate more reliable pseudo labels in ambiguous regions. Second, the class-wise dynamic pseudo-label filtering (CDPF) scheme uses lightweight 1D Gaussian mixture models to adaptively determine per-class thresholds, preserving challenging defects and suppressing spurious predictions. Comprehensive evaluations on the NEU-DET, GC10-DET, and PCB-DEFECT datasets show that TaDP-Det consistently outperforms state-of-the-art SSOD baselines in mean average precision (mAP) with only modest computational overhead. The results underscore the effectiveness of our method for robust semi-supervised defect detection in industrial applications.