Short Communication Open Access

Automatic Pipeline Threat Detection by Aerial Surveillance

Abstract

The pipeline industry has millions of miles of pipes buried along the length and breadth of the country. Since none of the areas through which pipelines run are to be used for other activities, it needs to be monitored so as to know whether the right-of-way of the pipeline is encroached upon at any point in time. Rapid advances made in the area of sensor technology have enabled the use of high end video acquisition systems to monitor the right-of-way of pipelines. Huge amounts of data are thus made available for analysis. However, it would be very expensive to employ analysts to scan through the data and identify threats along the right-of-way in the vast expanse of wide area imagery. This warrants the deployment of an automated mechanism that is able to detect threats and send out warnings in the event of any hint of a threat. The images captured by aerial data acquisition systems are affected by a host of factors that include light sources, camera characteristics, geometric positions and environmental conditions. UD Vision Lab is developing a multistage framework for the analysis of aerial imagery for automatic detection and identification of machinery threats along the pipeline right of way which would be capable of taking into account the constraints that come with aerial imagery such as low resolution, lower frame rate, large variations in illumination, motion blurs, etc. The visibility and features of objects may not be clear because of partial or total occlusion of light sources by buildings and trees which create a shadow. The complexity of large variations in the appearance of the object and the background in a typical image causes the performance degradation of detection algorithms. Our novel preprocessing technique improves the performance of automatic detection and identification of objects in an image captured in extremely complex lighting conditions.

Vijayan K. Asari

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