| Citation: | GOU J N，DU S S，WANG S D，et al. Insulator self-explosion detection in transmission line based on CenterNet fusing lightweight features[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2161-2171 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0602
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Intelligent inspection of transmission lines is an inevitable requirement for the construction of a new generation of power systems. At present, the detection model based on deep learning has too many parameters, which makes it difficult to deploy unmanned aerial vehicles (UAVs) at the edge. In order to enable the UAV to carry a lightweight model to identify insulators with self-explosion defects in transmission lines, a lightweight CenterNet-GhostNet target detection network was proposed. Firstly, the backbone feature extraction network of the model received lightweight treatment, and the multi-level features of insulators with self-explosion defects were extracted by using GhostNet with low computational costs, so as to reduce the complexity of the model. Then, the enhanced receptive field block (RFB) was introduced to enhance the ability of feature expression and enhance the attention of the model to the feature information of small targets. Finally, a feature fusion module was constructed to effectively fuse the low-level feature information and high-level feature information, so as to output a more complete feature map and improve the accuracy of defect recognition. The model training strategy of sharing transfer learning parameters and combining freezing and thawing training was used, so as to avoid insufficient generalization ability of the network caused by a small sample dataset. Based on the constructed dataset of insulators with self-explosion defects in transmission lines, the proposed method was verified. The experimental results show that compared with the original CenterNet, AP50, AP75, and AP50:95 of the proposed method are increased to 0.86, 0.74, and 0.63, respectively, and the number of model parameters is reduced from 124.61 ×106 to 64.2 ×106. Therefore, the proposed method can detect insulators with self-explosion defects in complex environments and improve the inspection accuracy and speed of transmission lines based on UAVs.
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