ABSTRACT
This research presents the development of a Frangi filter and first-order derivative of Gaussian (FF-FDOG) based power line detection (PLD) algorithm as an improvement to the standard PLD algorithm. Vision-based PLD is important in obstacle avoidance in low-altitude flight and also in the surveillance and maintenance of electrical infrastructure. The need for high and real-time detection rates as well as low false alarm in noisy and cluttered images makes it a challenging task. Matched filterand first-order derivative of Gaussian (MF-FDOG) based PLD algorithmwas developed to handle limitations associated with the standard PLD algorithm in terms of its ability to automatically select a problem specific threshold in its edge detection. The MF-FDOG based PLD, however, returned a high false positive rate and a detection rate insufficient for real time processing.The FF-FDOG based threshold is developed in this work using frangi filter (which detects vessel based on the eigen value analysis of the second order structure of an image) and FDOG filter. Images from the University of South Florida computer vision and pattern recognition group wire database were used to evaluate the performance of the developed FF-FDOG method. In the results obtained, the true positive rate of the developed FF-FDOG based PLD algorithm was 86.39%, which is a 2.64% improvement over MF-FDOG's 84.16%, while the false positive rate of the developed FF-FDOG based PLD algorithm was 11.45%, which is a 36.06% improvement over MF-FDOG's 17.91%.