PCNN Infrared Fault Region Detection Along Transmission Lines Based on the MST Framework

This paper presents a pulse-coupled neural network (PCNN) method for infrared fault region extraction based on maximum similarity thresholding to detect the fault region from the infrared image of a transmission line. In this method, the synchronous pulse characteristics of the PCNN model are used to cluster pixels via inner iteration, and the model is simplified by incorporating the maximum similarity thresholding method, enabling the PCNN model to simplify the thresholding setting. Meanwhile, the minimum clustering variance is introduced to set the linking coefficient. Thus, the PCNN model can efficiently segment an infrared image and obtain the effective thermal fault region in the image. The experimental results show that the proposed method exhibits good performance in region extraction and may be suitable for increasing the efficiency of automatic fault detection along transmission lines.