# Semi-Supervised Traffic Sign Detection with Dual Confidence Fusion Module and Structured Block-Regularized Neck

## TL;DR

This paper introduces a new semi-supervised learning framework for traffic sign detection that improves accuracy with limited labeled data.

## Contribution

A novel framework combining Dual Confidence Fusion and Structured Block-Regularized Neck for better pseudo-label filtering and detection accuracy.

## Key findings

- The proposed method achieves higher mAP50 scores than the baseline using 1% to 10% labeled data.
- The framework improves pseudo-label reliability and feature representation through dual confidence fusion and structured regularization.
- Results show consistent performance gains in complex traffic sign detection scenarios.

## Abstract

Reliable traffic sign detection is essential for the safety of autonomous driving systems. However, manually annotating large-scale datasets for this task is resource-intensive, making semi-supervised learning (SSL) a vital alternative. Despite their potential, current SSL methods often struggle with unreliable pseudo-label filtering and limited detection accuracy. To address these limitations, we propose a novel framework integrating a Dual Confidence Fusion (DC-Fusion) module and a Structured Block-Regularized Neck (SBR-Neck). The former improves pseudo-label reliability by combining classification and localization confidence scores, while the latter optimizes feature representation through multi-scale fusion and block-wise regularization. To preserve high-frequency spatial details, SBR-Neck incorporates Spatial-Context-Aware Upsampling (SCA-Upsampling), which utilizes multi-granularity feature decomposition. Experimental results on a proprietary traffic sign dataset demonstrate that our method achieves mAP50 scores of 10.4%, 17.8%, 23.7%, and 32.1% using 1%, 2%, 5%, and 10% labeled data, respectively. These results surpass the “Efficient Teacher” baseline by margins ranging from 3.07% to 11%, confirming the framework’s ability to provide robust detection in complex traffic scenarios.