Fault Detection of Insulators Using Second-order Fully Convolutional Network Model

As the traditional methods of insulator fault detection rely on the low-level feature extraction of images and classifier design, it is difficult to achieve fault detection of insulator for images with complex background. To address this issue, a fault detection method using second-order full convolutional network (SOFCN) is proposed in this paper. Firstly, the first-order FCN is used to learn the image features to segment insulator areas from images with complex background. Secondly, the mathematical morphology reconstruction operation is used to improve the segmentation result to get the accurate localization of the insulator areas. Finally, the FCN network is used again to detect the insulator fault and obtain the fault region. Experiments show that the proposed SOFCN is not only able to obtain accurate insulator region, but also able to effectively suppress the interference of noninsulator region. Compared to the conventional methods, the proposed SOFCN obtains higher recognition accuracy without feature extraction and the selection of a classifier. Moreover, the computational complexity of the proposed method is low. Furthermore, compared to the classical CNN and FCN segmentation methods, the proposed SOFCN can effectively suppress complex background interference to improve the accuracy of insulator fault detection.