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Training method, device, computer equipment and storage medium for target detection model

A target detection and training method technology, applied in the field of image recognition, can solve the problems of difficult training, poor signal data detection effect, etc., and achieve the effect of expanding the field of vision

Active Publication Date: 2022-07-05
CHENGDU UNION BIG DATA TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to improve the problem that the existing target detection algorithm has poor detection effect and difficult training for different types of signal data in the signal time-frequency diagram, this application proposes a training method, device, computer equipment and storage medium for a target detection model, which can Accurately detect different types of signal data in the signal time-frequency diagram, and the training and detection speed is fast, which can meet the real-time performance of signal detection

Method used

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  • Training method, device, computer equipment and storage medium for target detection model
  • Training method, device, computer equipment and storage medium for target detection model
  • Training method, device, computer equipment and storage medium for target detection model

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Embodiment 1

[0083] This embodiment provides a training method for a target detection model. like figure 1 As shown, the training method for a target detection model provided by this embodiment includes the following steps:

[0084] Step S110, acquiring a training set, where the training set includes multiple time-frequency graphs of signals.

[0085] The signal time-frequency map is usually a grayscale image. When the signal data in the signal time-frequency map is detected by the target detection model, the area where the signal exists is the signal target area, and the area where the signal does not exist is the noise area, and the signal target area. Usually there is a clear demarcation from the noise area. like figure 2 shown, figure 2 It is an example of a time-frequency diagram of a signal. The abscissa represents the time domain, and the ordinate represents the frequency domain. Some signal data with short duration and small frequency range are reflected on the signal time-fr...

Embodiment 2

[0165] This embodiment also provides a training device for a target detection model, please refer to Figure 5 , the target detection model training device 500 includes a first acquisition module 510 , a first construction module 520 , a first calculation module 530 , a second calculation module 540 , a second acquisition module 550 , a second construction module 560 , and an adjustment module 570 .

[0166] In this embodiment, the first obtaining module 510 is configured to: obtain a training set, where the training set includes multiple signal time-frequency diagrams;

[0167] The first construction module 520 is used for: constructing an initial detection model, where the initial detection model includes a backbone network and a head network;

[0168] The first calculation module 530 is configured to: input the multiple signal time-frequency diagrams into the backbone network, calculate the time-frequency diagrams of each signal through the backbone network, and output mult...

Embodiment 3

[0209] This embodiment provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the training method for the target detection model described in Embodiment 1 is implemented.

[0210] The computer device provided in this embodiment can implement the training method for the target detection model described in Embodiment 1, and to avoid repetition, details are not described herein again.

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Abstract

The embodiments of the present application disclose a training method, device, computer equipment and storage medium for a target detection model, which relate to the technical field of image recognition. The method includes: constructing a network structure of the target detection model, obtaining an initial detection model, inputting multiple signal time-frequency graphs into the initial detection model, outputting the final prediction result, constructing a loss function to calculate the training loss, and adjusting the initial detection model according to the training loss to obtain object detection model. In this application, the dilated convolution layer and the deconvolution layer are added to the network structure, and the negative sample weight value is added to the loss function, so that the target detection model can more smoothly distinguish the real target area and the blank target area during training. In this way, the signal data in the signal time-frequency diagram can be quickly trained and detected, and various types of signal data can be accurately detected.

Description

technical field [0001] This solution belongs to the technical field of image recognition, and in particular relates to a training method, device, computer equipment and storage medium for a target detection model. Background technique [0002] In order to ensure the reliability of information transmission, the information transmission system must have stable anti-interference ability, and signal detection is one of the best methods to resist interference. The existing signal detection scheme is the time-frequency analysis method, the process of which is as follows: a one-dimensional signal is mapped to a two-dimensional plane to generate a time-frequency map of the signal. Using deep neural network to detect target signal data in signal time-frequency map is called target detection problem. The YOLO algorithm, the YOLOV3 algorithm, and the Poly-YOLO algorithm can be used for target detection on the signal time-frequency map. When the YOLO algorithm performs target detection...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F2218/12G06F18/23213G06F18/214Y02T10/40
Inventor 不公告发明人
Owner CHENGDU UNION BIG DATA TECH CO LTD
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