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Every company, no matter the business sector, looks for ways to streamline costs and increase productivity. The utility infrastructure landscape isn’t any different. From helicopter flights for electric line systems that are difficult to reach by ground transportation to physical sight “eyes on” inspection practices, both are being challenged by the speed, capability and accuracy of a drone program. For example, a trained worker can perform a series of inspection functions at the base of the pole capturing GPS coordinates, images, and measurement heights of the pole attachments including electric conductor and fiber communications. This time and resource heavy inspection activity is generally carried out by an employee physically walking pole to pole and manually capturing their observations. In comparison, a UAS outfitted with a LiDAR camera capable of capturing special imagery can perform this same function in a fraction of the time by measuring distances to objects from the given location at upwards of 30 thousand points a second and with greater accuracy. This time saving method can also deliver an accurate result including elevation information that can be used for future system design and tree trimming diagnostics for a forestry line clearing program.
You do not have to be an expert in data acquisition to recognize the large amount of raw data that can be captured from a single flight operation. From location based GPS and sensor results to image and video capture in multiple formats, sorting through the data and retaining what is valuable can be a daunting task. One of the growth areas that play a role in analytics is machine learning for image analysis. Having a system in place that can tell an operator what to look for based on the data captured changes the scenario from an employee scanning images or video, to a system identifying abnormal conditions based on a defined set of parameters for review. It could be a utility pole leaning at an unsafe angle that needs replacement or a heat signature identified through a thermal camera showing a transformer near overload conditions. Leveraging technology removes the risk of human error and variability in the data captures; however, the risk of data paralysis remains a challenge that industry and academic institutions are only now beginning to tackle.










