Improving Vision-Based Freight Vehicle Detection in Smart Cities Using Generative Adversarial Networks

Abstract

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.