The failure of an insulator may compromise the
safety of the entire power transmission system. Therefore, insulator defect detection is vital for the safe operation of power
systems. However, insulator defects in an insulator image may
have varying sizes, and several currently available methods do not
have satisfactory detection accuracy for small defects. To address
this issue, we propose an improved detection network for small
insulator defects with a batch normalization convolutional block
attention module (BN-CBAM) and a feature fusion module. The
BN-CBAM is designed to better exploit channel information and
enhance the effect of different channels on the feature map. In
addition, we propose a feature fusion module that fuses multiscale features from different layers to improve small object
detection performance. Moreover, to address the scarcity of aerial
images, a data augmentation method based on the fusion of
the target segment and background is introduced. Experiments
demonstrate that the proposed method achieves better small
insulator defect detection performance than other state-of-theart approaches. In addition, data augmentation methods enrich
sample diversity and enhance the generalizability of the network.