Improving Pothole Recognition through Vision Tracking for Automated Pavement Assessment

Pavement condition assessment is essential when developing road network maintenance programs. In practice, pavement sensing is to a large extent automated when regarding highway networks. Municipal roads, however, are predominantly surveyed manually due to the limited amount of expensive inspection vehicles. As part of a research project that proposes an omnipresent passenger vehicle network for comprehensive and cheap condition surveying of municipal road networks this paper deals with pothole recognition. Existing methods either rely on expensive and high-maintenance range sensors, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In our previous work we created a pothole detection method for pavement images. In this paper we present an improved recognition method for pavement videos that incrementally updates the texture signature for intact pavement regions and uses vision tracking to track detected potholes. The method is tested and results demonstrate its reasonable efficiency.

Novel field emission from graphene sheets supported by CNTs arrays

A good quality graphene is transferred onto honeycomb-like CNTs arrays with inner supporting CNTs. The efficient field emission is demonstrated due to a high aspect ratio protrusions and graphene crack edges. A high efficient current density about 1.2 mA/cm2 at threshold electric field of 7.8 V/μm with a turn-on electric field of 1.8 V/μm at the current density of 10 μA/cm2 is observed due to high localized electric field. Stable field emission is tested in a vacuum chamber. The results are of significance to the development of Graphene based field emitters. © 2013 IEEE.