The detection of insulators with cluttered backgrounds in aerial images is a challenging task for an automatic transmission line inspection system. In this paper, we propose an effective and reliable insulator detection method based on a deep learning technique for aerial images. In the proposed deep detection approach, the single shot multibox detector (SSD), a powerful deep meta-architecture, is incorporated with a strategy of two-stage fine-tuning. The SSD-based model can realize automatic multi-level feature extractor from aerial images instead of manually extracting features. Inspired by transfer learning, a two-stage finetuning strategy is implemented using separate training sets. In the first stage, the basic insulator model is obtained by fine-tuning the COCO model with aerial images, including different types of insulators and various backgrounds. In the second stage, the basic model is fine-tuned by the training sets of the specific insulator types and specific situations to be detected. After the two-stage fine-tuning, the well-trained SSD model can directly and accurately identify the insulator by feeding the aerial images. The results show that both the porcelain insulator and composite insulator can be quickly and accurately identified in the aerial images with complex background. The proposed approach can enhance the accuracy, efficiency, and robustness significantly. INDEX TERMS Insulator detection, deep learning, single shot multibox detector (SSD), fine-tuning.

No abstract

No abstract

No abstract

No abstract