Abstract
In northern countries, ice storms can cause major power disruptions such as the one that occurred on December 2013 that left more than 300,000 customers in Toronto with no electricity immediately after such an ice storm. Detection of ice formation on power cables can help on taking actions for removing the ice before a major problem occurs. A computer vision solution was developed to detect ice on difficult imaging scenarios such as images taken under fog conditions that reduces the image contrast, passing cars that are within the field of view of the camera as well as different illumination problems that can occur when taking images during different times of the day. Based on a neural network for classification and six image features that can deal with these difficult images, we reduced the errors on a set of images that was previously yielding 20 errors out of 50 images to only one error.
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