Feature GANs: A Model for Data Enhancement and Sample Balance of Foreign Object Detection in High Voltage Transmission Lines

The suspension of foreign objects on high-voltage transmission lines is extremely harmful to the safety of the line. If it is not handled in time, it will easily cause phase-to-phase short circuit of the transmission line and even cause forest fires. Foreign object suspension is a small probability event with fewer existing samples. To use CNN to perform target classification detection, there is a problem of insufficient sample or sample imbalance. Aiming at the above problems that often occur in engineering applications of CNN, we propose a data enhancement algorithm based on GANs. The main idea of this algorithm is: Firstly, the pre-training model is used to extract the feature map of sample, and GANs is used to learn the feature map directly. Then, the feature map generated by GANs and the original data are used to train the classification layer of the pre-training model, so as to achieve the purpose of data enhancement and balancing samples, and then enhance the classification ability of the model. The experimental results show that the classification performance of several classical CNN models can be improved significantly by using this method in the case of insufficient sample and sample imbalance.