NSVD: Vehicle Image Dataset from Northern Slovakia Under Diverse Weather Conditions

Abstract

Existing vehicle detection models, typically trained on general-purpose or non-regional datasets, frequently underperform when applied to local traffic monitoring systems that rely on fixed roadside cameras. Changes in viewpoint, object size, local vehicle types and road conditions creates a domain mismatch between the training and target data. In addition, general datasets lack a dedicated class for van-bodied vehicles, despite their relevance to traffic monitoring. To address these limitations, we introduce the Northern Slovakia Vehicle Dataset (NSVD), specifically designed to capture European vehicle types. NSVD comprises nearly 8000 human-reviewed images collected from four fixed roadside sites in northern Slovakia and annotated with four visual weather conditions. We evaluate the transfer of You Only Look Once (YOLO) and Real-Time Detection Transformer (RT-DETR) models from general object detection to this roadside setting, and we introduce an evaluation protocol that accounts for the differing vehicle class definitions used in the source and target datasets. Training on NSVD improved detection across all evaluated classes and object sizes. The performance differences among the leading fine-tuned models were smaller than the improvement obtained from training on the target data. Results for individual weather conditions must be interpreted jointly with site and class composition, and they are therefore reported as descriptive findings. A leave-one-site-out evaluation shows that the impact of excluding a site from training ranges from minimal at one roadside location to substantial at the city-center site. NSVD provides a public benchmark for the development and transparent evaluation of vehicle detectors intended for fixed roadside cameras.