Abstract
A moveable lane barrier along the Auckland Harbour Bridge (AHB) enables two-way traffic flow optimisation and control. However, the AHB barrier transfer machines are not equipped with an automated solution for screening of the pins that link the barrier segments. To improve traffic safety, the aim of this paper is to combine traditional machine with deep learning approaches to aid visual pin inspection. For model training with imbalanced dataset, we have included additional synthetic frames depicting unsafe pin positions produced from collected videos. Preliminary experiments on produced models indicate that we are able to identify unsafe pin positions with precision and recall up to 0.995. To improve traffic safety beyond the AHB case study, future developments will include extended datasets to produce near-real time IoT alerting solutions using mobile and other video sources.
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Acknowledgement
The authors wish to thank Gary Bonser, Angela Potae and Martin Olive from NZ Transport Agency and Auckland System Management for their assistance including safety briefings, transport to the site, video recording under their supervision, various problem insights project requirements, availability and ongoing enthusiasm to assist with this project. We also wish to express our gratitude for the comprehensive documentations provided by the contributors to ImageNet, TensorFlow, Orange Data Mining, Google Cloud, MathWorks, OpenCV and SqueezeNet and for sharing their tools and libraries.
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Bačić, B., Rathee, M., Pears, R. (2020). Automating Inspection of Moveable Lane Barrier for Auckland Harbour Bridge Traffic Safety. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_13
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DOI: https://doi.org/10.1007/978-3-030-63830-6_13
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