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Dial plate detection method and system based on multi-task cascaded convolutional network

A convolutional network, multi-task technology, applied in the field of dial detection based on multi-task cascaded convolutional network, can solve problems such as increasing manual operations, and achieve improved accuracy, good effect, good universality and robustness Effect

Inactive Publication Date: 2020-02-04
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

However, traditional hard sample mining usually adopts an offline method, which greatly increases the manual operation

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  • Dial plate detection method and system based on multi-task cascaded convolutional network
  • Dial plate detection method and system based on multi-task cascaded convolutional network
  • Dial plate detection method and system based on multi-task cascaded convolutional network

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[0049] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0050] First, some technical terms involved in the present invention are explained.

[0051] Overlap degree IoU: For a sub-target image of an image, the predicted frame of the sub-target image is calibrated, and a certain correlation between the final calibrated predicted frame and the natural frame (usually manually calibrated) of the real sub-image is called IoU (Intersection over Union), the...

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Abstract

The invention discloses a dial plate detection method and system based on a multi-task cascaded convolutional network, and belongs to the technical field of target detection. An instrument panel areain the whole picture is detected and feature points in the instrument panel are detected and positioned; a multi-task learning mechanism is adopted, the relation between sub-tasks is fully utilized, dual tasks of instrument panel area detection and instrument panel feature point positioning are achieved in parallel, a new online difficult sample mining strategy is used, and the training effect isbetter than that of manual sample selection. A corresponding training model is generated by training a data set, and dial detection and dial feature point positioning are performed on a test set picture by using the training model so that accurate detection of the dial area and accurate positioning of the dial feature points under different backgrounds can be realized and the effect of real-time detection can be achieved by the lightweight convolutional neural network design.

Description

technical field [0001] The invention belongs to the technical field of target detection, and more specifically relates to a dial detection method and system based on a multi-task cascaded convolutional network. Background technique [0002] The automatic extraction of instrument dial readings is one of the deepest and most extensive research topics in the industrial inspection field. The dial gauge is simple in structure, easy to maintain, and free from magnetic field interference. Due to its high reliability and low price, it has been widely used in scientific experiments and industrial production. The results of these instruments are usually obtained manually, which is time-consuming and labor-intensive, and can lead to interpretation errors. Slower reading speed, lower efficiency, and higher repetition rates can have a significant impact on the accuracy of results. Therefore, how to use the computer system to automatically and accurately read the results of the dial in...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V2201/02G06N3/045G06F18/241G06F18/214
Inventor 韩守东王俊凯郑丽君陈阳
Owner HUAZHONG UNIV OF SCI & TECH
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