Intelligent detect for substation insulator defects based on CenterMask

TL;DR

This paper introduces an advanced, end-to-end insulator defect detection method for substations using an improved CenterMask framework, enhancing accuracy and speed in processing complex images for intelligent maintenance.

Contribution

It proposes a novel insulator defect detection approach based on CenterMask with improved backbone network and spatial attention, achieving higher accuracy in complex backgrounds.

Findings

Effective segmentation of insulators in complex backgrounds.

Accurate localization of defect points.

Robustness verified on real substation images.

Abstract

With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this paper proposes an intelligent detection method for substation insulator defects based on CenterMask. First, the backbone network VoVNet is improved according to the residual connection and eSE module, which effectively solves the problems of deep network saturation and gradient information loss. On this basis, an insulator mask generation method based on a spatial attentiondirected mechanism is proposed. Insulators with complex image backgrounds are accurately segmented. Then, three strategies of pixel-wise regression prediction, multi-scale features and centerness are introduced. The anchor-free single-stage target detector accurately locates the defect points of insulators. Finally, an example analysis is carried out with the substation inspection image of a power supply company in a certain area to verify the effectiveness and robustness of the proposed method.