Development of a time-synchronised multi-input computer vision system for structural monitioring utilising deep learning for vehicle identification

Bridges are a key component of this infrastructure, if a bridge fails or is unnecessarily closed it has widespread adverse effects throughout the surrounding area. Detailed monitoring is essential to ensure adequate maintenance of these structures is carried out, this is not currently the case as many bridges are only sporadically checked by visual inspection, often by a junior engineer.

Structural Health Monitoring (SHM) has been developed to counteract this shortfall, to date the instrumentation used has primarily been contact based and usually requires bridge closure. Computer Vision is the process of using cameras to obtain data from images, this method is now being applied to monitor civil structures worldwide. With regards to the monitoring of bridge displacement from applied vehicle load, the primary focus of existing research has been on single camera studies to monitor one point on the bridge without a means of identifying the cause of the measured displacement.