Substation pointer instrument identification method based on improved YOLOV3 model

A recognition method and pointer technology, applied in the field of computer vision, can solve the problems of long training time, poor detection effect of multiple targets and small targets, and large hardware consumption, so as to improve real-time performance, improve detection and recognition effect, and improve accuracy. degree of effect

Active Publication Date: 2020-04-24
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

However, the traditional YOLOV3 training takes too long, consumes a lot of hardware, and the detection effect on multi-target and small targets is not good.

Method used

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  • Substation pointer instrument identification method based on improved YOLOV3 model
  • Substation pointer instrument identification method based on improved YOLOV3 model
  • Substation pointer instrument identification method based on improved YOLOV3 model

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Embodiment Construction

[0053] A detailed description will be given below with reference to the accompanying drawings and the technical solutions in the embodiments of the present invention.

[0054] like figure 1 The overall flow chart of the substation pointer meter identification method based on the improved YOLOV3 model is shown in the overall flow chart, including creating a data set, clustering, constructing a new frame network, and using the new loss function to train the network for training, and finally get the results quickly.

[0055] The present invention is based on the Anaconda Prompt console environment under the 64-bit system of Windows 10 and the 3.6 version of python. All the auxiliary functions in the scheme and the original YOLOV3 structure are based on the 2.2.1 version of the keras framework, and the activation functions and machine learning algorithms belong to the keras callable library functions.

[0056] The method of the present invention specifically comprises the followi...

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Abstract

The invention discloses a substation pointer instrument identification method based on an improved YOLOV3 model, and the method comprises the following steps: firstly collecting an instrument image, making a data set, and carrying out the calibration; then, clustering the bounding box through a Mini Batch Kmeans algorithm to find an optimal clustering coordinate; modifying a framework network DarkNet-53 of the basic YOLOV3 into a lightweight network MobileNet, and accelerating a training process by virtue of a better activation function; modifying a loss function of coordinate prediction to enable the model to better fit instrument data; and finally, enabling the trained model to be better applied to the detection and identification task of the substation inspection robot, and a small-target instrument panel and a multi-target instrument panel can be quickly and accurately obtained for subsequent processing in the detection process. On the premise that the accuracy is guaranteed, the detection speed is increased, the real-time performance is enhanced, and the detection effect on small targets and multiple instrument panels is greatly improved.

Description

technical field [0001] The invention relates to the field of computer vision and the technical field of target detection, in particular to a detection and identification method of a pointer-type instrument in an unattended substation. Background technique [0002] The pointer meter has the characteristics of low manufacturing cost, wide industrial application, simple structure, high precision and easy maintenance, etc. It is widely used in scientific research and large-scale industrial production testing. These precision instruments require periodic calibration to maintain the high accuracy of the instrument. In the past, the traditional method of pointer instrument panel detection is to observe the position through the human eye, and this method may cause errors or even errors due to personal reasons and environmental reasons, causing great losses to the company. First of all, there are differences between the eyes of different people, and the resolution of the human eye i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/10G06V2201/02G06F18/23213G06F18/214
Inventor 吴怀宇刘家乐陈洋李想成
Owner WUHAN UNIV OF SCI & TECH
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