Research on infrared image recognition algorithm of power transmission equipment based on improved cascade R-CNN

Transmission lines are an important guarantee for the continuous power supply of power grids. In order to ensure their safe operation and timely and effectively detect potential heating anomalies, this paper proposes an infrared image recognition algorithm for transmission equipment based on improved Cascade R-CNN. The introduction of deformable convolutional networks and classifier enhancement algorithms improves the target recognition capability of infrared images of power transmission equipment. Contrast enhancement and GAN networks are used to improve image quality and alleviate sample imbalance. Experiments show that the algorithm can achieve an accuracy rate of over 87% for various component recognition and over 98% for insulator recognition, greatly improving the quality of detection, providing strong support for the diagnosis of heating defects, and having important application value for the safe maintenance of power grids.