Detecting Insulator Strings as Linked Chain Structure in Smart Grid Inspection

Abstract

In high-voltage power systems, insulators are essential components in transmission lines for increasing shooting distance and securing wires. Unmanned aerial vehicle (UAV) imaging becomes a common way of inspecting the state of the insulators. However, the automatic detection of insulators with complex backgrounds is still a challenging task. Most of the existing object detection methods are based on anchors, which do not have sufficient ability to describe objects which have a string-like structure. To tackle it, inspired by the keypoints-based object detection method, we propose a novel chain structure framework to detect insulators. First, we model the string-like object as a chain structure consisting of keypoints and their linkage, by which the insulator strings features are efficiently encoded and trained in the proposed ChainNet. Then, an assembling algorithm is proposed to assemble the estimated keypoints and linkages into chains, by which the insulator strings can be tightly enclosed in rotational bounding boxes. We evaluate the proposed approach on our collected large-scale rigorous dataset under the Directed Intersection over Union (DIoU) metric.The extensive experimental results show that the proposed method achieves 75.7% mAP, which yields 3.4, 15, and 10.6 improvements to the state-of-the-art rotational anchor, axial-aligned anchor, and anchor-free detection methods, respectively. Moreover, the proposed framework can easily be extended to detect other string-like manmade objects in the industrial area. The code is available at: https://github.com/XCLXY0/Insulators