Power transmission and transformation line insulator aerial image fault detection method based on improved YOLOv3

A technology for power transmission and transformation lines and fault detection, which is used in image analysis, image enhancement, image data processing, etc. It can meet the needs of industrial applications, and cannot meet the real-time and accuracy of power transmission and transformation line inspection, so as to achieve the effect of real-time inspection, detection effect and detection speed improvement, and detection effect guarantee.

Active Publication Date: 2019-07-19
ELECTRIC POWER RES INST STATE GRID SHANXI ELECTRIC POWER +1
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional target detection algorithm mainly has three defects. First, the designed features are low-level features, which are not expressive enough for the target; The robustness is not very good; finally, for the current mass inspection pictures, the detection speed and detection effect cannot meet the actual industrial application requirements
[0004]Deep learning has developed rapidly since 2012 and has achieved go

Method used

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  • Power transmission and transformation line insulator aerial image fault detection method based on improved YOLOv3
  • Power transmission and transformation line insulator aerial image fault detection method based on improved YOLOv3
  • Power transmission and transformation line insulator aerial image fault detection method based on improved YOLOv3

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Embodiment 1

[0033] Please refer to Figure 1-3 , the present invention provides a technical solution: a fault detection method based on the improved YOLOv3 aerial image fault detection method for power transmission and transformation line insulators, comprising the following steps:

[0034] S1: Establish an insulator data set and an insulator fault data set. The images of the insulator data set are aerial images of various types of insulators of power transmission and transformation lines captured by drones in accordance with inspection specifications at specific positions. The insulator fault data set includes Images of various insulator faults such as self-explosion and leakage, the image is the image of the faulty insulator area cropped from the initial aerial image and maintains the original size;

[0035] S2: Use rotation, flipping, contrast enhancement, etc. to perform data enhancement; use the LabelImg tool to mark the image to obtain the corresponding XML file, and then organize a...

Embodiment 2

[0048] Please refer to Figure 1-3 , the present invention provides a technical solution: a fault detection method based on the improved YOLOv3 aerial image fault detection method for power transmission and transformation line insulators, comprising the following steps:

[0049] S1: Establish an insulator data set and an insulator fault data set. The images of the insulator data set are aerial images of various types of insulators of power transmission and transformation lines captured by drones in accordance with inspection specifications at specific positions. The insulator fault data set includes Images of various insulator faults such as self-explosion and leakage, the image is the image of the faulty insulator area cropped from the initial aerial image and maintains the original size;

[0050] S2: Use rotation, flipping, contrast enhancement, etc. to perform data enhancement; use the LabelImg tool to mark the image to obtain the corresponding XML file, and then organize a...

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Abstract

The invention discloses a power transmission and transformation line insulator aerial image fault detection method based on improved YOLOv3. The method comprises the following steps of S1, establishing an insulator data set and an insulator fault data set; s2, performing data enhancement by using modes of rotation, overturning, contrast enhancement and the like; s3, taking YOLOv3 as a basic framework, carrying out model modification pruning operation, and constructing an insulator detection model; s4, establishing a standard YOLOv3-tiny network; s5, carrying out model training on the modifiedYOLOv3 network and the modified YOLOv3-tiny by using the insulator data set and the insulator fault data set; and S6, connecting the two trained models to realize end-to-end rapid detection of the insulator fault. According to the detection method, the insulator in the complex background image can be quickly and accurately identified, the fault area can be positioned and detected, the inspection efficiency is improved, the working intensity of inspection personnel is reduced, and a guarantee is provided for normal operation of a power system.

Description

technical field [0001] The invention belongs to the field of deep learning computer vision and the field of electric defect identification, and in particular relates to an aerial image fault detection method for power transmission and transformation line insulators based on improved YOLOv3. Background technique [0002] There are a large number of insulators in power transmission and transformation lines, which play an important role in electrical insulation and mechanical connection. Because the power transmission and transformation lines span various complex natural and geographical environments and are exposed to wind, rain and sun for a long time, they are prone to various failures including self-explosion and flashover. Once an insulator fails, it will seriously affect the normal and safe transmission of power, thus threatening the normal operation of the power system. Therefore, fast and efficient fault detection of insulators is one of the important procedures for the...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/10004G06T2207/20081
Inventor 杨罡李永祥曹京津王欣伟王天正张兴忠
Owner ELECTRIC POWER RES INST STATE GRID SHANXI ELECTRIC POWER
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