Nondestructive inspection of electrical insulators subjected to the high electrical stress and environmental damage is fundamental for reliable operation of a transmission lines. The breakage and defect of the insulator have great influence on the safe of transmission lines, and insulator defect detection with difference types is a complex work. This paper proposed an insulator defect detection method inspired by human receptive field model, which meets the requirements for detecting defect insulator in a simple background. In this method, the defect detection combined human receptive field model of human visual system is constructed and applied on the different insulators, so as to achieve accurate detection of the insulator defected parts. Experimental results show that the method can accurately and robustly detect the defect (such as cracks and damage) of electrical insulator in case of noise affect.
Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines
