A Survey of Data Augmentation Techniques for Traffic Visual Elements

TL;DR

This paper reviews and compares data augmentation methods for traffic visual elements, showing how they improve model robustness and performance in autonomous driving.

Contribution

A structured taxonomy and benchmark for traffic data augmentation, including evaluation of diffusion models and hybrid approaches.

Findings

Hybrid augmentation methods often yield the best performance improvements in traffic element detection.

Diffusion models and multimodal approaches show promise in generating rare driving scenarios.

Key challenges include computational costs and unstable GAN training for traffic data.

Abstract

What are the main finding?

We propose a structured taxonomy specifically for the enhancement of traffic visual elements data, integrating techniques such as image transformation, Generative Adversarial Networks (GANs), Diffusion Models, and composite methods.

We construct a comprehensive cross-comparison benchmark encompassing nearly 40 datasets and 10 evaluation metrics, which systematically reveals the performance of different augmentation strategies across key metrics including accuracy, mean average precision (mAP), and robustness.

We demonstrate the capability of emerging generative paradigms, particularly diffusion models and multimodal composite models, in representing rare driving scenarios, and analyzes their trade-offs between computational cost and semantic consistency.

What are the implications of the main findings?

This paper systematically consolidates diverse data augmentation strategies within the domain of traffic visual elements, thereby charting a forward-looking roadmap for researchers engaged in developing perception models.

This paper employs a multi-layered analytical framework to systematically link augmentation strategies to real-world performance outcomes, thereby providing methodological guidance for future evaluation and research.

This paper identifies the enhancement of data reliability and cross-domain transferability in intelligent transportation systems as a critical future direction, which is paramount for the successful deployment of autonomous driving technologies.

Autonomous driving is a cornerstone of intelligent transportation systems, where visual elements such as traffic signs, lights, and pedestrians are critical for safety and decision-making. Yet, existing datasets often lack diversity, underrepresent rare scenarios, and suffer from class imbalance, which limits the robustness of object detection models. While earlier reviews have examined general image enhancement, a systematic analysis of dataset augmentation for traffic visual elements remains lacking. This paper presents a comprehensive investigation of enhancement techniques tailored for transportation datasets. It pursues three objectives: establishing a classification framework for autonomous driving scenarios, assessing performance gains from augmentation methods on tasks such as detection and classification, and providing practical insights to guide dataset improvement in both research and industry. Four principal approaches are analyzed, including image transformation, GAN-based generation, diffusion models, and composite methods, with discussion of their strengths, limitations, and emerging strategies. Nearly 40 traffic-related datasets and 10 evaluation metrics are reviewed to support benchmarking. Results show that augmentation improves robustness under challenging conditions, with hybrid methods often yielding the best outcomes. Nonetheless, key challenges remain, including computational costs, unstable GAN training, and limited rare scene data. Future work should prioritize lightweight models, richer semantic context, specialized datasets, and scalable, efficient strategies.

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