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

## TL;DR

This paper introduces TaDP-Det, a new semi-supervised method for detecting surface defects in industrial settings using texture-aware pseudo-labeling.

## Contribution

The novel approach combines texture enhancement and dynamic label filtering to improve pseudo-label quality in semi-supervised defect detection.

## Key findings

- TaDP-Det outperforms existing semi-supervised object detection methods on multiple industrial defect datasets.
- The proposed Texture Enhance Module improves pseudo-label reliability in ambiguous regions.
- Class-wise dynamic filtering enhances detection accuracy by adapting to defect-specific challenges.

## Abstract

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.