A Decision Support System for Power Components Based on Improved YOLOv4-Tiny

Yangyang Tian,Wandeng Mao,Diming Wan,Yuanhui Chen,Shaoguang Yuan,Zeeshan Pervez

doi:10.1155/2021/4447271

Yangyang Tian, Wandeng Mao + Show 4 more

Open Access

https://doi.org/10.1155/2021/4447271

Copy DOI

Abstract

The traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.

Highlights

As an infrastructure related to the national economy and people’s livelihood, the power system is very important to modern society [1, 2]. erefore, it is a very essential task to monitor whether the power components work safely and reliably. e traditional manual detection method requires people to work for a long time, and its effect is related to the experience and working status of the staff. e system is not capable of continuous monitoring and is less reliable
Traditional target detection algorithms mainly use artificially designed features, such as Haar classifier [10, 11], cascaded classifier [12], SIFT [3, 4, 13,14,15], HOG [16], DPM [17], SVM [18], and so on, or combine them [19, 20]. ese methods’ [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] disadvantages are as follows: (1) eir feature extractors are manually selected and not robust to changes in complex practical application scenarios. (2) eir region selection strategies are based on sliding windows, with high time complexity and window redundancy, which affects the accuracy and speed of detections
With the rapid development of neural networks and artificial intelligence technology, there have been a large number of deep learning-based object detection algorithms applied to power inspection [5,6,7,8,9]. ese methods can be roughly divided into two categories

Summary

Introduction

As an infrastructure related to the national economy and people’s livelihood, the power system is very important to modern society [1, 2]. erefore, it is a very essential task to monitor whether the power components work safely and reliably. e traditional manual detection method requires people to work for a long time, and its effect is related to the experience and working status of the staff. e system is not capable of continuous monitoring and is less reliable. E traditional manual detection method requires people to work for a long time, and its effect is related to the experience and working status of the staff. (2) eir region selection strategies are based on sliding windows, with high time complexity and window redundancy, which affects the accuracy and speed of detections. With the rapid development of neural networks and artificial intelligence technology, there have been a large number of deep learning-based object detection algorithms applied to power inspection [5,6,7,8,9]. One is two-stage methods that search for the boxes first and perform classification and regression, whose accuracy is higher but speed is slow, such as the R-CNN series algorithm based on Region Proposal

Methods

Results

Conclusion