An Electrical Insulator Defects Detection Method Combined Human Receptive Field Model

Abstract

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.

Electrical insulator defect detection with incomplete annotations and imbalanced samples

“…For the small-scale insulators, all methods correctly identify and accurately locate those at positions (1)-( 3) and (7). The detection errors of each method are mainly concentrated on positions (5) and (13). Primarily, the "Broken" insulator at position ( 5) is misidentified as "Good" by M-YOLO v3, D-YOLO v3, and YOLO v5, misclassed as "FlashDamaged" by D-YOLO v4, and repeatedly identified as both "Good" and "Broken" by Pi-FT. Only M-YOLO v4 and the method proposed in this paper manage to correctly identify it.…”

“…Primarily, the "Broken" insulator at position ( 5) is misidentified as "Good" by M-YOLO v3, D-YOLO v3, and YOLO v5, misclassed as "FlashDamaged" by D-YOLO v4, and repeatedly identified as both "Good" and "Broken" by Pi-FT. Only M-YOLO v4 and the method proposed in this paper manage to correctly identify it. Next, the "Broken" insulator at position (13) was missed by M-YOLO v3, M-YOLO v4, and YOLO v5. It was misidentified as "Good" by D-YOLO v3 and "FlashDamaged" by D-YOLO v4.…”

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

“…In the man-made feature-based methods, multiple features including color [11,14], shape [15][16][17], edge [18,19], gradient [20], texture [21], key-points [22][23][24][25] and their fusions [26,27] have been explored. Meanwhile, some mathematical models have also been applied, such as the snake model [28], Hough transform [29], Active Contour Model [30], Fuzzy c-means [31], and Receptive field model [32]. In the work of Wang [14], a threshold filtering scheme based on Lab color space is proposed to locate the insulators in aerial images.…”

Intelligent Controller Design of the Vehicle Sonar Tracking Checker Fault and Repair of High Voltage Transmission Line

“…Helicopter-assisted assessments are faster, but because they are unable to wander more than a short distance from the control frameworks, their visual frameworks or any other sensor will be unable to provide reliable estimates following that, an unmanned ethereal vehicle-based robot plan is proposed here to investigate and implement a few critical upkeep techniques for various control framework components. The paper [3] shows the defect detection result of an electrical insulator with simulated crack. It gives the edge detection of original ceramic electrical insulators without crack and defects ceramic electrical insulators with crack.…”