Image-based 3D reconstruction and recognition for enhanced highway condition assessment

Frequent and accurate condition assessment is essential for an effective transportation system operation and asset management. Despite the importance, current manual data collection methods for highway assets are time consuming, subjective and sometimes unsafe. There is a need for an automated and efficient data collection method that does not have a significant cost impact and can achieve automation, accuracy, and safety in condition assessment. Over the past few years, advances in technology such as cheap and high-resolution digital cameras and availability of vast data storage has allowed a number of computer vision models to be developed that can detect and assess condition of some individual assets. However, none of these vision-based methods recognize, locate, assess condition of the assets, and visualize their most updated status in a 3D environment. This paper proposes a new approach, based on 3D image-based reconstruction and integrated recognition of color, shape, and texture for highway assets, and presents preliminary results from the developed system on a real world case study.