Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

Yuquan Chen,Xingwei Zhang,Hongxing Wang,Xiaowei Gao,Jie Shen,Shah Nazir

doi:10.1155/2021/9976209

Yuquan Chen, Xingwei Zhang + Show 4 more

Open Access

https://doi.org/10.1155/2021/9976209

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Abstract

Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

Highlights

In recent years, deep learning methods produced an effective method for big data processing, and it has made a breakthrough in many different fields such as automatic speech recognition and target recognition [1,2,3,4,5]
[28] is selected as the backbone network, and different initial training parameters are selected in the experiment, such as solver type, initial learning rate, and learning step size. e network optimization function selected in this experiment is Stochastic Gradient Descent (SGD) [29], the learning step is set to 2, the image scaling scale is (1222, 800), the NMS threshold is 0.5, and the initial learning rate is 0.001
In equations (4) and (5), True Positives (TP) represent the number of defective targets that are correctly classified; FP represents the number of background interferences that are mistakenly regarded as defective targets; False Negatives (FN) represents that the defective targets are incorrectly classified as background quantity

Summary

Introduction

Deep learning methods produced an effective method for big data processing, and it has made a breakthrough in many different fields such as automatic speech recognition and target recognition [1,2,3,4,5]. With the development of the intelligent industry, deep learning technology has begun to emerge in smart grid image recognition and defect detection applications [8,9,10]. Antwi-Beko uses Convolutional Neural Network (CNN) to detect and classify defective insulators in transmission line images, achieving high-precision defect. Wang Yixing trains the stackable automatic encoder (SAE) to initialize and train the deep learning neural network and observes the hidden features of defects from different dimensions, so as to make a preliminary judgment of defects [19]. In order to learn the model incrementally and adapt to unlabeled data, we use the improved Faster RCNN to initialize and train the deep learning neural network and observe the hidden features of defects from different dimensions. The deformable convolution and feature pyramid modules are added to the training algorithm to greatly improve the feature extraction capabilities

Materials and Methods

Data-Driven Training System

Results and Discussion