LAUSR

Insulator defect is one of the most important factors for the grid power transmission accidents. However, up till now, traditional insulator defect identification methods cannot meet the requirements of high-speed transmission and high pixel ratio aerial image processing. To solve this problem, in this paper, we proposed a novel method based on CenterMask algorithm to achieve intelligent insulator defect identification. First, the overall architecture of the proposed method that entirely relies on the deep learning models is designed to map the relationship between inputs and outputs. Subsequently, the residual connection and effective Squeeze-Excitation module are introduced to improve the original backbone network, thus overcoming the problem of deep network saturation and channel information loss in the feature layer. Finally, the SAG-Mask with spatial attention mechanism is performed to extract the insulator mask image, while the defect identification and location is realized based on the anchor-free FCOS algorithm. At last, we verify the performance of this proposed method by comparing with other benchmarks, including YOLOv4, SSD and Faster RCNN, which shows high accuracy and good robustness of CenterMask-based insulator defect identification algorithm.