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Exploring the Potential of Synthetic Data for Bike Path Surface Classification using Diffusion Models

Year: 2025

Type: article

Abstract: Road surface classification of bike paths enables image recognition applications for bike route planning, navigation optimization or path maintenance. However, acquiring and annotating real data can be costly and time-consuming. Synthetic data can overcome data scarcity and annotation costs. We use synthetic data, generated by Stable Diffusion to improve neural network performance on new or unseen surfaces. We compare model performance for different real-synthetic data ratios. Our results show that synthetic data decreases the amount real data needed and improves neural network performance in road surface classification on new surfaces.

Source: Repository of Futwangen University of Applied Sciences (Furtwangen University)

DOI: 10.60643/urai.v2023p198

Authors: Göttisheim, Valentin, Ziekow, Holger, Schanbacher, Peter, Taminé, Oliver, Baier, Jochen, Ould-Abdelsam, Djafar

Institutions: Furtwangen University, Université de Haute-Alsace

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6920237261

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