Contrastive Learning-Driven Traffic Sign Perception: Multi-Modal Fusion of Text and Vision

TL;DR

This paper introduces a novel multi-modal framework combining open-vocabulary detection and contrastive learning to improve traffic sign recognition, especially for small, rare, and scale-variant signs in real-world scenarios.

Contribution

It proposes a two-stage framework with a specialized detection model and a contrastive learning approach that enhances recognition of long-tail and multi-scale traffic signs.

Findings

Achieves 78.4% mAP on TT100K for long-tail detection

Attains 91.8% accuracy and 88.9% recall in recognition tasks

Outperforms mainstream algorithms in complex scenarios

Abstract

Traffic sign recognition, as a core component of autonomous driving perception systems, directly influences vehicle environmental awareness and driving safety. Current technologies face two significant challenges: first, the traffic sign dataset exhibits a pronounced long-tail distribution, resulting in a substantial decline in recognition performance of traditional convolutional networks when processing low-frequency and out-of-distribution classes; second, traffic signs in real-world scenarios are predominantly small targets with significant scale variations, making it difficult to extract multi-scale features.To overcome these issues, we propose a novel two-stage framework combining open-vocabulary detection and cross-modal learning. For traffic sign detection, our NanoVerse YOLO model integrates a reparameterizable vision-language path aggregation network (RepVL-PAN) and an SPD-Conv module to specifically enhance feature extraction for small, multi-scale targets. For traffic sign classification, we designed a Traffic Sign Recognition Multimodal Contrastive Learning model (TSR-MCL). By contrasting visual features from a Vision Transformer with semantic features from a rule-based BERT, TSR-MCL learns robust, frequency-independent representations, effectively mitigating class confusion caused by data imbalance. On the TT100K dataset, our method achieves a state-of-the-art 78.4% mAP in the long-tail detection task for all-class recognition. The model also obtains 91.8% accuracy and 88.9% recall, significantly outperforming mainstream algorithms and demonstrating superior accuracy and generalization in complex, open-world scenarios.