Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines

Abstract

Bolts are the most numerous fasteners in transmission lines and are prone to losing their split pins. How to realize the automatic pin-missing defect detection for bolts in transmission lines so as to achieve timely and efficient troubleshooting is a difficult problem and the long-term research target of power systems. In this article, an automatic detection model called automatic visual shape clustering network (AVSCNet) for the pin-missing defect is constructed. First, an unsupervised clustering method for the visual shapes of bolts is proposed and applied to construct a defect detection model that can learn the difference in visual shape. Next, three deep convolutional neural network optimization methods are used in the model: the feature enhancement, feature fusion, and region feature extraction. The defect detection results are obtained by applying the regression calculation and classification to the regional features. In this article, the object detection model of different networks is used to test the data set of pin-missing defect constructed by the aerial images of transmission lines from multiple locations, and it is evaluated by various indicators and is fully verified. The results show that our method can achieve considerably satisfactory detection effect.