  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
trouble shooting is a difficult problem and the long-term research target of
power systems. In this paper, an automatic detection model called Automatic
Visual Shape Clustering Network (AVSCNet) for pin-missing defect is
constructed. Firstly, an unsupervised clustering method for the visual shapes
of bolts is proposed and applied to construct a defect detection model which
can learn the difference of 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 paper, the object detection model of different
networks is used to test the dataset 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.
