Insulator Fault Detection in Aerial Images Based on Ensemble Learning With Multi-Level Perception

Abstract

Insulator fault in the transmission lines is the main factor of power transmission accident. The images captured from the aerial inspection can be utilized to detect the fault of insulators for further maintenance. For automatic transmission lines inspection system, the insulator fault detection is an interesting and challenging task due to the complex background and diversified insulators. In this paper, we propose a novel insulator fault detection method based on multi-level perception for aerial images. The multi-level perception is implemented by an ensemble architecture which combines three single-level perceptions. These single-level perceptions include the low level, middle level, and high level that are named by the attention to the insulator fault. They detect the insulator fault in the entire image, multi-insulator image, and single-insulator image, respectively. To address the filtering problem in the combination of three single-level perceptions, an ensemble method is proposed for generating the final results. For training the detection models employed in the multi-level perception, a powerful deep meta-architecture so-called single shot multibox detector (SSD) is utilized. The well-trained SSD models can automatically extract high quality features from aerial images instead of manually extracting features. By using the multi-level perception, the advantages of global and local information can achieve a favorable balance. Moreover, limited inspection images are fully utilized by the proposed method. Fault detection recall and precision of the proposed method are 93.69% and 91.23% testing in the practical inspection data, and insulator fault under various conditions can be correctly detected in the aerial images. The experimental results show that the proposed method can enhance the accuracy and robustness significantly.