Nondestructive inspection of electrical insulators subjected to the high electrical stress and environmental damage is fundamental for reliable operation of a transmission lines. The breakage and defect of the insulator have great influence on the safe of transmission lines, and insulator defect detection with difference types is a complex work. This paper proposed an insulator defect detection method inspired by human receptive field model, which meets the requirements for detecting defect insulator in a simple background. In this method, the defect detection combined human receptive field model of human visual system is constructed and applied on the different insulators, so as to achieve accurate detection of the insulator defected parts. Experimental results show that the method can accurately and robustly detect the defect (such as cracks and damage) of electrical insulator in case of noise affect.
High-voltage transmission lines and transmission towers in China are usually in a harsh environment where their components are often eroded by rain or damaged by unpredictable foreign objects. When an electrical insulator is in operation at the high-voltage transmission line systems, those devices are subjected to a strong electrical stress and also damaged by the severely environmental conditions [
Electrical safe inspection captured by UAV Imaging [
The paper is organized as follows. In Section
A large number of biological experiments show that the primary visual pathway of the human brain can obtain the main information of the object and play a key role in the overall perception of the object [
Illustration of Basic Human Receptive Field (RF).
Basic processing pathway of human visual system
Illustration of classical receptive field. (The left is center-on LGN cells and the right is center-off LGN cells.)
For the above details, the proposed electrical insulator defect detection method is inspired by human brain RF model, which is followed in [
The process of detection and illustration of constructed model.
We denote by
The concentric receptive field of a single LGN cell can be characterized as (
The appropriate selection of parameter
Selection of the appropriate parameter n and orientation comparison.
Original image
n=8
n=12
n=16
n=8
n=12
n=16
0 degrees
101 degrees
214 degrees
270 degrees
326 degrees
The configured detection model cell response to a natural image is illustrated in Figure
In this section, the evaluation performance of the proposed model in the electrical insulator defect detection task is shown. Although several types of insulators are used in China, this paper only showed two main types of insulators: one type is the white ceramic insulator and the other is the dark red insulator.
For the reason of lacking public electrical insulator dataset, all of the electrical insulators used in our experiment are from our team’s captured dataset. The dataset includes 300 images, there are 34 images are good electrical insulator images, and others are defect electrical insulator images. Figure
Some electrical insulator images in our dataset.
There are three experiments are adopted in this paper, which include simulated defect detection, real defect detection, and robust feature detection. All of these experimental results can sufficiently show the efficiency of the proposed detection method.
Simulated defected detection of ceramic electrical insulator.
Real defected detection of ceramic electrical insulator.
Experiments in Section
Robust detection test of original circle and different noisy insulator.
The application of image processing and machine learning method is a popular development trend in future electricity power transmission line inspections. Inspired by human brain visual pathway LGN and V1 simple cell RF characteristic, this paper proposed an electrical insulator defect detection method combined computational model in area V1 of visual cortex, different feature orientation selectivity is achieved by combining operation of a collection of LGN cells with center-surround RF. Demonstration of different experimental results shown that the proposed method can achieve accurate electrical insulator defect detection, even complete robust detection of noisy insulators. Because the background of the insulator image acquired by aerial photography is complicated and there is more than one insulator type, defects are difficult to detect, so that our further work will focus on optimizing this method, identifying and locating the insulator defect by segmentation, and extending it to more wide application. The aim is greatly improving the efficiency of electricity power transmission line and easy to find defect or fault in the system, so as to provide completely and timely guarantee for electricity grid dispatching and electrical equipment maintenance.
Because the original datasets are captured by our research group, the datasets generated during and /or analysed during the current study are available from the corresponding author on reasonable request.
The authors declare that they have no conflicts of interest.
This work is supported by National Natural Science Foundation of China (61663008), Natural Science Foundation of Hubei Province (2015CFC781, 2014CFB612), and Ph.D. Technology Program of Hubei University for Nationalities (MY2014B018).