Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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.
Recommended Citation
Xu, Shihan; Mccarthy, Chris; Jayaraman, Prem Prakash; Ghaderi, Hadi; and Dia, Hussein, "Improving Vision-Based Freight Vehicle Detection in Smart Cities Using Generative Adversarial Networks" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/da/smart_city/3
Improving Vision-Based Freight Vehicle Detection in Smart Cities Using Generative Adversarial Networks
Hilton Waikoloa Village, Hawaii
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.
https://aisel.aisnet.org/hicss-58/da/smart_city/3