Real-Time Automated Survey System of Pavement Cracking in Parallel Environment

As a part of the digital highway data vehicle (DHDV), the automated survey system developed at the University of Arkansas is the implementation of a real-time system for pavement surface cracking survey. The researchers faced tremendous tasks in optimizing imaging algorithms to speed up the processing at the same time without sacrificing accuracy in identifying and classifying cracks. This paper introduces the automated real-time system and summarizes the experiences in developing parallel algorithms in imaging processing used in the real-time system. The hardware system for processing images is based on the ubiquitous multiple Central Processing Unit (CPU) $\times 86$ platform that has the capability of two levels of parallel processing at multiple CPU level and within each CPU level. The former is commonly called Symmetrical Processing (SMP) and the latter is called Single Instruction Multiple Data (SIMD). The paper also presents results of a network level survey with the DHDV and the distress analyzer on a network of about $161\mathrm{km}$ $\left(100\mathrm{mil}\right)$ of pavements. In addition, a manual survey was conducted on the same network of pavements. World Bank’s universal cracking indicator (CI) is used in this study. Because the distress analyzer is fully automated and results of the analysis are provided in synch with image collection, the potential cost savings when compared with manual survey methods and other semiautomated survey technologies are tremendous.