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Image target detection method and system based on lightweight neural network model

A neural network model and object detection technology, which is applied in the field of image object detection, can solve problems such as high cost of high-performance GPU, inapplicable model deployment, and inability to move, so as to meet real-time performance and accuracy, reduce model size, and improve accuracy Effect

Pending Publication Date: 2022-04-12
QILU UNIV OF TECH
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Problems solved by technology

[0006] However, high-performance GPUs are relatively expensive and relatively immovable. They are only suitable for model training, but not suitable for model deployment and actual production and life applications.

Method used

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  • Image target detection method and system based on lightweight neural network model
  • Image target detection method and system based on lightweight neural network model
  • Image target detection method and system based on lightweight neural network model

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Embodiment approach

[0037] In the case of no conflict, the embodiments and the features in the embodiments of the present invention can be combined with each other.

[0038] The current mainstream research direction of the target detection algorithm based on deep learning is to continuously reduce the weight of the neural network model, and continuously improve the operation ability of the model on small devices and mobile terminals, so that the model can be better applied to actual production and life. more socioeconomic benefits.

Embodiment 1

[0040] This embodiment discloses an image target detection method based on a lightweight neural network model. This implementation example takes smoking behavior detection as an example for illustration, and of course it can also be applied to image detection of other target behaviors.

[0041] Include the following steps:

[0042] S1: Make a data set for deep learning training and testing, and divide and process the entire data set;

[0043] S2: Configure the python and pytorch programming environment for neural network model training and testing;

[0044] S3: Construct the backbone network and feature fusion network required to implement the yolov5 target detection algorithm, where the backbone network is used to extract useful features in the image to be detected, and the feature fusion network is used to strengthen the useful features extracted by the backbone network and output the image to be detected The final feature map of ;

[0045] S4: Define the loss function of ...

Embodiment 2

[0120] The purpose of this embodiment is to provide an image target detection system based on a lightweight neural network model, including:

[0121] The data input module is configured to: input the path of the picture or video to be detected;

[0122] The target detection module is configured to: use the lightweight neural network model to calculate the relative confidence of all classifications in the received current image, and select the highest confidence to obtain the final recognition frame and draw it in the original picture to complete the detection process.

[0123] The present invention relates to neural network, deep learning, machine vision, and target detection technology. It mainly uses the latest single-stage target detection algorithm yolov5, adjusts the width (width_multiple) and depth (depth_multiple) of the network structure, and adjusts the backbone network (backbone ) to improve Conv and CSPNet, effectively reducing the complexity of the model while ensu...

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Abstract

The invention provides an image target detection method and system based on a lightweight neural network model, belongs to the technical field of image target detection, solves the problem of inaccurate image recognition at present, and comprises the following steps: inputting a path of a to-be-detected picture or video; and calculating related confidence coefficients of all classifications in the received current image by using the lightweight neural network model, obtaining a final recognition frame by selecting the highest confidence coefficient, and drawing the recognition frame in the original image to complete the detection process. Under the condition that the model precision is ensured, the operation speed of the model is greatly improved, so that the model can be smoothly deployed and applied in small equipment and a mobile terminal, and the real-time performance and accuracy of smoking detection in a daily scene are met.

Description

technical field [0001] The invention belongs to the technical field of image target detection, and in particular relates to an image target detection method and system based on a lightweight neural network model. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The task of Object Detection is to find out all the objects (objects) of interest in the image and determine their categories and positions, which is one of the core issues in the field of machine vision. Object detection has always been the most challenging problem in the field of machine vision due to the different appearance, shape and posture of various objects, coupled with the interference of factors such as illumination and occlusion during imaging. [0004] Existing studies have shown that reliable object detection algorithms are the basis for automatic analysis and understa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/20G06V20/40G06N3/04G06N3/08
Inventor 刘海英孙凤乾邓立霞郑太恒王超平
Owner QILU UNIV OF TECH
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