Improving Vision-Based Freight Vehicle Detection in Smart Cities Using Generative Adversarial Networks
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2025-01-07
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1256
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A freight activity grows within urban areas, accurately estimating their impact becomes crucial for effective planning and modelling of transport infrastructure. Reliable Origin-Destination (OD) information is essential for strategic transport models, which guide future infrastructure investments and sustainable urban development. In this paper we propose a Generative Adversarial Network (GAN)-based domain adaptation approach for accurately detecting heavy vehicles from low-quality surveillance data under varying lighting conditions. To the best of our knowledge, this is the first study to use GAN-based data augmentation for freight vehicle movement analysis in smart cities, contributing to the development of more efficient, scalable, and reliable smart city planning solutions.
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Decision Support for Smart City, computer vision, freight detection, generative adversarial networks, smart city
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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