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Target detection method and system based on dual-stage convolutional neural network

A convolutional neural network and target detection technology, which is applied in the field of target detection methods and systems based on two-stage convolutional neural networks, can solve problems such as inability to accurately reflect actual losses and calculation method deviations, and achieve real-time calculations and high accuracy High, improve the effect of time-consuming and laborious

Pending Publication Date: 2022-01-21
JILIN UNIV
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Problems solved by technology

Since these four parts of loss are not independent of each other, there is an interdependence relationship in actual calculation, so there is a deviation in this loss calculation method, which cannot accurately reflect the actual loss caused by the center coordinates and width and height
For example, there may be different center abscissa, center ordinate, width and height losses while the total center coordinates and width and height losses are the same

Method used

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  • Target detection method and system based on dual-stage convolutional neural network
  • Target detection method and system based on dual-stage convolutional neural network
  • Target detection method and system based on dual-stage convolutional neural network

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Embodiment Construction

[0041] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0042] like figure 1As shown, a target detection method based on a two-stage convolutional neural network includes the following steps:

[0043] S1. Obtain a video stream in the detection area, and preprocess the video stream to obtain multiple frames of pictures;

[0044] S2, inputting the multiple frames of the frame picture into the pre-trained YOLOv3 target detection improved model for target detection, and obtaining the frame picture with the detection target;

[0045] The present invention realizes the parking detection function of the detection target based on the two-stage target detection and tracking model. By using the improved target detection algorithm, the real-time position information of the same de...

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Abstract

The invention relates to a target detection method and system based on a dual-stage convolutional neural network. The method comprises the following steps: acquiring a video stream in a detection area, and preprocessing the video stream to obtain a plurality of frame pictures; and inputting the multiple frames of frame pictures into a pre-trained YOLOv3 target detection improved model for target detection to obtain a frame picture with a detection target. According to the invention, the improved YOLOv3 dual-stage convolutional neural network is adopted to monitor the target in real time, the real-time position information of the same moving object is obtained more accurately, the calculation error of multiple targets and overlapped targets is avoided, the real-time tracking of the moving multi-target object is realized, and the detection accuracy is high.

Description

technical field [0001] The invention relates to the field of parking detection, in particular to a target detection method and system based on a two-stage convolutional neural network. Background technique [0002] Hazardous chemical transport vehicles, referred to as vehicles, are extremely dangerous because their loads and unloads are usually flammable, explosive or highly toxic substances. Therefore, they must be parked at designated locations in strict accordance with the management measures for hazardous chemical vehicles. During transportation, it is not allowed to park at will, and temporary parking is not allowed near open flames, high-temperature places, crowded places and other places that may cause harm. In view of the problem that hazardous chemical vehicles may cause serious harm if they are not parked according to regulations during road transportation, especially in chemical industrial parks, [0003] At present, based on pure target detection algorithms, suc...

Claims

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Application Information

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IPC IPC(8): G06V10/774G06V20/40G06V20/52G06K9/62G06N3/04G06T7/11G06T7/90
CPCG06T7/11G06T7/90G06T2207/20132G06N3/045G06F18/214
Inventor 李若楠
Owner JILIN UNIV