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ABSTRACT
Distribution lines which are used in power transmission are prone to certain defects which might develop over time and use. Since overhead lines are exposed, they are also vulnerable to damage caused by outside forces. One of such defects occurs when a line stretches such that the gap between it and the adjacent line or the ground falls below acceptable standards. This is known as an Overhead Line Sag Defect. This leads to reduced ground clearance and the increased chance of conductors swinging into contact. Left unattended, such faults can cause problems including power outages and equipment damage so there is need for them to be quickly detected and attended to by maintenance personnel. This paper proposed a method of detecting such defects on a distribution line using computer vision; in particular, the Mask R-CNN architecture. The purpose of this research is to develop an automated approach to handling this task in an efficient, safe and timely manner. We look at literature reviews and works relating to using AI and CNN to detect and solve different problems as well as where these systems have been effective and their limitations. The proposed methodology, design process, expected results, project timeline and cost analysis are also discussed.
