E-VarifocalNet: A Lightweight Model to Detect Insulators and Their Defects under Power Grid Surveillance

Abstract

Detecting insulators and their defects is a key task in real-time power grid surveillance with the rapid development of national smart grid. Traditional surveillance usually relies on maintenance personnel, leading to the issues of inefficiency and unsafety. Thus, with the prosperity of deep learning, we proposed a detection algorithm, named E-VarifocalNet, which is an enhanced version of the basic VarifocalNet method. The proposed E-VarifocalNet is specifically designed for detecting insulators and their defects. We developed a classification loss based on varifocal loss and the number of samples to solve the imbalance problem in object detection. Furthermore, a regression loss based on GIoU loss and Wasserstein distance is designed to gain higher flexibility in the representation of bounding boxes. Additionally, we applied a feature pyramid network based on dilated convolution and heatmap to build global and local semantic relations among pixels so as to enhance the detection accuracy on salient areas. Our dataset containing 2,100 images and 5,217 object instances was collected through real-time drones and an open data platform. Our E-VarifocalNet gets the highest mAP and a low model complexity on our dataset among state-of the-art object detectors, indicating the potential of our algorithm in real-time power grid surveillance applications.