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Abstract:

Smart cities increasingly rely on computer vision systems to understand and respond to local urban dynamics. However, the effectiveness of such systems depends on datasets tailored to local environments and needs, which are often scarce or unavailable. Data scarcity arises from regional variability, privacy concerns, and the high cost of data collection and annotation. This paper proposes a synthetic-data generation and validation framework for rapidly developing locally adaptive perception models in smart-city environments. Using electric scooter detection as a representative case study, we demonstrate how diffusion-based generative models can create realistic, diverse, and privacy-preserving images to supplement limited real-world datasets. We evaluate three state-of-the-art diffusion models, perform a comparative analysis, and report on average 6% improvement in detection performance across these models when training YOLO-based object detectors. Results show that synthetic augmentation substantially improves detection accuracy in data-scarce scenarios, validating the utility of synthetic data for local adaptation. The resulting model is further deployed on an edge device as a proof of concept, illustrating the feasibility of lightweight, privacy-aware, and adaptable vision systems for next-generation smart cities.

Authors:

By Martinraj Arulmani Nadar, Hadise Pishdast, Hari Kalva & Velibor Adzic

Conference / Journal

2026 IEEE International Conference on Consumer Electronics (ICCE)