Power transmission line multi-target detection method

A transmission line and detection method technology, applied in the field of transmission line target recognition, can solve the problems of consuming a lot of manpower, material resources, financial resources, high risk, and poor results

Pending Publication Date: 2021-01-05
LIAONING TECHNICAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The use of traditional inspection methods will consume a lot of manpower, material resources, and financial resou

Method used

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  • Power transmission line multi-target detection method
  • Power transmission line multi-target detection method
  • Power transmission line multi-target detection method

Examples

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Embodiment Construction

[0063] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0064] In this example, 2,300 electric power background pictures published on Github are used as experimental data, and the sample expansion technology and the Cascade R-CNN detection model based on ResNet101 and 6-layer FPN network are used to realize three different types of insulators and two types of insulator defects on transmission lines , feature recognition and localization of anti-vibration hammers, interphase rods and bird nests.

[0065] like figure 1 As shown, the method of this embodiment is as follows.

[0066] Step 1: Use the sample expansion algorithm to expand the data of 2300 electric background pictures into 18620 picture data. The specific sample expa...

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Abstract

The invention discloses a power transmission line multi-target detection method which mainly performs target identification on three types of insulators, two types of insulator defects, a shockproof hammer, an interphase rod and a bird nest, and belongs to the technical field of power transmission line target identification. The method comprises the following steps: firstly, increasing the order of magnitudes of sample data by utilizing a sample generation technology, enhancing the detection effect of deep learning, then dividing newly generated experimental data into a training set, a test set and a verification set, constructing a PyTorch deep learning environment, and establishing ResNet101 and 6 layers of FPN networks to extract image features by adopting four paths of GPU distributedtraining; wherein the output of the ResNet101 and six layers of FPN networks serves as the input of the RPN network to train a Cascade R-CNN deep learning network model, and finally target recognitionis achieved according to a Softmax classifier and a frame regression result. The method is high in operation speed, high in target recognition accuracy and high in multi-target recognition capability.

Description

technical field [0001] The invention relates to the technical field of transmission line target recognition, in particular to a transmission line multi-target detection method. Background technique [0002] Ensuring the reliability of transmission lines is an important part of smart grid construction, and it is also the basis for safe and stable operation of power systems. my country's transmission lines are composed of line towers, wires, insulators, anti-vibration hammers, pull wires, tower foundations, grounding devices, etc., among which insulators and anti-vibration hammers are vulnerable parts. It is easy to build nests on poles and towers. On the one hand, it will cause the line to fail to operate normally, and on the other hand, it may cause serious electrical failures in extreme weather. Therefore, in order to ensure the safe and stable operation of the power system, it is very important to identify the target of the transmission line and repair or replace it in tim...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30108G06N3/047G06N3/045G06F18/241G06F18/2415
Inventor 李鑫刘帅男杨桢李艳王珂珂宋阳李钰梁
Owner LIAONING TECHNICAL UNIVERSITY
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