Insulator defect detection with deep learning: A survey

With the improvement of smart grid, utilizing unmanned aerial vehicles (UAV) to detect the operation status of insulators has attracted widespread attention. The insulator defects can lead to serious power loss, damage the service life of power lines, and even result in power outages in serious cases. The small-scale object, complex background, and limited-number collected data make insulator defect still a challenging problem. Benefitted by the advances in deep learning, deep learning-based insulator defects have achieved great progress in recent years. In the paper, the authors present a novel systematic survey of these advances, where further analysis about different processing stages methods is introduced: (i) insulator processing stage methods exploit the specific image pre-processing algorithm for data augmentation and low-level vision information extraction; (ii) defect detection stage model can locate and classify diagnosis fault with different task targets, like sequential task strategy and multi-task strategy. In addition, the authors also review publicly available benchmark and datasets. The future research direction and open problem are discussed to promote the development of the community.

Automated summary

With the advancement of smart grid technology, the use of unmanned aerial vehicles (UAV) for detecting insulator operation status has gained significant attention. Defects in insulators can cause power loss, damage power line durability, and even lead to power outages. However, due to the small-scale objects, complex background, and limited data, detecting insulator defects remains a challenging task. This paper presents a comprehensive survey on the recent progress of deep learning-based methods for insulator defect detection. The authors also discuss different processing stage methods, including image preprocessing algorithms for data augmentation and low-level vision information extraction, as well as defect detection stage models for fault diagnosis.