CN114332666A - A method and system for image target detection based on lightweight neural network model - Google Patents

Abstract

The invention proposes an image target detection method and system based on a lightweight neural network model, which belongs to the technical field of image target detection, and solves the problem of inaccurate image recognition at present, including: inputting the path of the picture or video to be detected; The quantitative neural network model calculates the relevant confidence levels of all categories in the received current image, and obtains the final recognition frame by selecting the highest confidence level and drawing it in the original image to complete the detection process. Under the condition of ensuring the accuracy of the model, the present invention greatly improves the running speed of the model, so that the model can be smoothly deployed and applied on small devices and mobile terminals, and meets the real-time and accuracy of smoking detection in daily scenarios.

Description

A method and system for image target detection based on lightweight neural network model

technical field

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

The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

The task of Object Detection is to find out all the objects of interest (objects) in the image and determine their category and position, which is one of the core problems in the field of machine vision. Due to the different appearance, shape and pose of various objects, coupled with the interference of factors such as illumination and occlusion during imaging, object detection has always been the most challenging problem in the field of machine vision.

Existing studies have shown that reliable target detection algorithms are the basis for automatic analysis and understanding of complex scenes. Therefore, image target detection is a basic task in the field of computer vision, and its performance will directly affect the performance of subsequent middle and high-level tasks such as target tracking, action recognition, and behavior understanding, which in turn determines face detection, behavior description, and traffic scene objects. Performance of subsequent AI (artificial intelligence) applications such as recognition, content-based Internet image retrieval, etc. As these AI applications penetrate into all aspects of people's production and life, target detection technology has reduced the burden on people to a certain extent and changed the way of life of people.

In recent years, with the continuous update and iteration of GPUs (graphics processing units) with high-performance computing capabilities, the development of target detection algorithms based on deep learning has been extremely rapid. The single target detection algorithm represented by yolo, through the blessing of neural network and deep learning, has obtained strong image feature extraction and fusion capabilities. Compared with the traditional sliding window target detection algorithm, it has stronger performance, stability and stability. Generalization.

However, high-performance GPUs are relatively expensive and cannot be moved. They are only suitable for model training, but not for model deployment and actual production and life applications.

SUMMARY OF THE INVENTION

In order to overcome the above-mentioned shortcomings of the prior art, the present invention provides an image target detection method based on a lightweight neural network model, which greatly improves the running speed of the model under the condition of ensuring the accuracy of the model, so that the model can be used in small Devices and mobile terminals are deployed and applied smoothly to meet the real-time and accuracy of detection in daily scenarios.

To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:

In a first aspect, an image target detection method based on a lightweight neural network model is disclosed, including:

Enter the path of the image or video to be detected;

The light weight neural network model is used to calculate the relevant confidence levels of all categories in the received picture to be detected, and the final recognition frame is obtained by selecting the highest confidence level and drawn in the to-be-detected picture to complete the detection process.

In a further technical solution, the lightweight neural network model includes a backbone network and a feature fusion network;

The backbone network uses convolution to process the image to be detected to generate a real feature layer, and then performs linear transformation on the real feature layer to obtain a phantom feature layer;

The feature fusion network further processes and fuses the to-be-detected image information processed in the phantom feature layer to generate a feature pyramid, and the feature pyramid is used for enhanced detection of objects of different scales and identification of the same object of different sizes and scales.

In a further technical solution, the lightweight neural network model training process is:

Get a dataset containing the target behavior;

Among them, the data set containing the target behavior comes from the open source data set downloaded from the network. For the unlabeled data set that has been obtained, use the labeling tool to label: select the subjects that can be identified after learning and training, and then The position and length and width data of the main body relative to the image size are stored in the corresponding XML file, and the file name corresponds to the image file;

The labeled data set is randomly divided into training data set and test data set. The training data set is used for training the lightweight neural network model, and the test data set is used for the testing of the lightweight neural network model.

A further technical solution is to encapsulate the images of the training data set in a fixed size and format and then transfer them into the constructed backbone network and feature fusion network to obtain the prediction result of the lightweight neural network model;

Calculate the loss of the output of the lightweight neural network model and the real value, calculate the gradient of the loss value, and finally update the parameters of the lightweight neural network model with the gradient descent algorithm, and adjust the model parameters by finding the optimal solution of the loss function.

A further technical solution is to process the marked test data set, convert the XML format file into a txt format file that can be read correctly, and adjust the corresponding file name and directory in the test data set.

In a further technical solution, the backbone network and the feature fusion network are used to run the yolov5 target detection algorithm.

In a further technical solution, the lightweight neural network model is applied to real-time detection of smoking scenes in daily life.

In a further technical solution, each stage of the feature fusion network path takes the feature map of the previous stage as an input, and processes it with a convolution layer, and the output is added to the same stage feature map of the top-down path through side connections. , these feature maps provide information for the next stage of processing; the feature maps of different layers are fused together after adaptive pooling operation, and then detected to generate the relevant confidence and prediction of each prediction category in the image to be detected. Border position information.

In a further technical solution, the backbone network is mainly composed of Conv modules and CSPNet modules, and the number of Conv modules and CSPNet modules in the entire neural network is adjusted by controlling the width and depth of the network structure.

In a second aspect, an image target detection system based on a lightweight neural network model is disclosed, including:

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

The target detection module is configured to: use a lightweight neural network model to calculate the relevant confidence levels of all categories in the received picture to be detected, and obtain the final recognition frame by selecting the highest confidence level and draw it in the to-be-detected picture to complete the detection. process.

One or more of the above technical solutions have the following beneficial effects:

The invention applies the yolov5 target detection algorithm to the real-time detection of the smoking scene in daily life. Adjust the width (width_multiple) and depth (depth_multiple) of the default network structure of yolov5, reduce the size of the entire model for smoking detection, and improve the running speed of the model. At the same time, two network modules, Ghost-Conv and C3-Ghost, are combined to improve the default model. After the improvement, the size of the model is reduced by 46% and the accuracy is increased by 2%. Under the condition of ensuring the accuracy of the model, this improvement greatly improves the running speed of the model, so that the model can be smoothly deployed and applied on small devices and mobile terminals, meeting the real-time and accuracy of smoking detection in daily scenarios.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will become apparent from the description which follows, or may be learned by practice of the invention.

Description of drawings

The accompanying drawings forming a part of the present invention are used to provide further understanding of the present invention, and the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention.

Figure 1 is a partial network structure diagram of the yolov5 backbone network (backbone);

FIG. 2 is a partial network structure diagram of a yolov5 backbone network (backbone) according to a modified embodiment of the present invention.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present invention.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

The current mainstream research direction of target detection algorithms based on deep learning is the continuous lightweight neural network model, which continuously improves the running ability of the model on small devices and mobile terminals, so that the model can be better applied to actual production and life, creating a more socioeconomic benefits.

Example 1

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

Include the following steps:

S1: Make a dataset for deep learning training and testing, and divide and process the entire dataset;

S2: Configure the python and pytorch programming environments for neural network model training and testing;

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 ;

S4: Define the loss function of the yolov5 target detection algorithm (classification loss and localization loss);

S5: Adjust the width (width_multiple) and depth (depth_multiple) of the default network structure of yolov5, reduce the size of the entire model for smoking detection, and improve the running speed of the model. At the same time, two network modules, Ghost-Conv and C3-Ghost, are combined to improve the default model. After the improvement, the size of the model is reduced by 46% and the accuracy is increased by 2%. Under the condition of ensuring the accuracy of the model, this improvement greatly improves the running speed of the model, so that the model can be smoothly deployed and applied on small devices and mobile terminals to meet the real-time performance of target detection;

S6: Use the prepared data set to train the neural network model, when the loss function does not converge, calculate the overall performance of the model under the current number of training rounds, and retain the optimal model file after multiple training;

S7: Test the optimal model after light weight to ensure that the effect is real and effective.

The processing procedure of step S1 is as follows:

S11: The data set used in the training process of this algorithm is 4860 smoking behavior data sets, of which the data sets are mainly from network downloads and open source data sets that have been marked by others. Only the cigarettes in the data set are marked. When smoking behavior is detected, the main body of the cigarette is marked as smoking, otherwise it will not be processed;

S12: The data set labeling format adopted in the present invention is XML format, and the labeling tool labeling is used to label the obtained unlabeled data set. The main process is: select the subject that you want the algorithm to recognize after learning and training, and then save the position and length and width data of the subject relative to the image size in the corresponding XML file, and the file name corresponds to the image file;

S13: Randomly divide the labeled dataset of 4860 images, of which 3791 images are used for the training process of the neural network and 1069 images are used for the testing process. The training process is used to obtain the final neural network model, and the testing process is used to test the reliability and accuracy of the model;

S14: For the labeled smoking detection data set, convert the XML format file into a txt format file that can be read correctly by pytorch, and adjust the corresponding file name and directory according to the program.

The processing procedure of step S2 is as follows:

S21: The present invention mainly builds a virtual environment through pycharm. Pycharm is a programming software developed for python, which has powerful code writing and management capabilities, and can also enrich plug-ins, extensions and third-party program packages. At the same time, pycharm has its own virtual programming environment setting function. When we choose to create a new virtual environment at the same time when we create a new project, we can construct a programming environment that is completely isolated from the local area, which is conducive to our subsequent code writing and debugging;

S22: The present invention adopts the pytorch deep learning framework for training and development. After the virtual environment is created in the above process, the local python interpreter is selected. Then install the pytorch main framework and other necessary third-party scientific computing and auxiliary function implementation packages. The present invention uses python3.9, pytorch1.9+cu111;

S23: The deep learning neural network model training process will generate a large amount of parallel computing, so during the training process, GPU devices with high-performance parallel computing capabilities are required to accelerate model training. The present invention uses Nvidia RTX2060 GPU to train and develop the target detection algorithm. In order to ensure the compatibility of the equipment and ensure that the GPU can normally participate in the calculation process, the cuda and cudnn drivers need to be installed, and the version number of the cuda and cudnn drivers selected by the present invention is 11.1;

The processing procedure of step S3 is as follows:

The network structure of S31:yolov5 is mainly divided into a backbone network (backbone) and a feature fusion network (head). The backbone network mainly uses the CSPNet (Cross Stage Partial Networks) cross-stage local network module, which is used to extract the image to be detected. Useful features, and at the same time, the network structure can have better performance while reducing the amount of computation. CSPNet solves the problem of gradient information repetition and gradient disappearance in network optimization in the Backbone layer of other neural network models, reducing the number of parameters of the model and the FLOPS (model complexity index) value, which not only ensures the inference speed and accuracy, but also reduces the Model size.

CSPNet consists of several partial dense layers and one partial transition layer. The processing process of the image to be detected by the cross-stage local network represented by CSPNet can be expressed by the following formula:

（1）

(1)

（2）

(2)

（3）

(3)

（4）

(4)

（5）

(5)

（6）

(6)

k in the formula represents the number of layers of the partial dense layer, X _k represents the feature map output by the partial dense layer of the kth layer, and W _k and g _k represent the network weight and gradient of the partial dense layer of the kth layer, respectively. X _T , W _T and g _T represent the feature map, network weight and gradient output by some transition layers, respectively, and X _U and W _U represent the final output feature map and network weight of CSPNet, respectively.

All the steps involved in CSPNet are as follows:

将输入CSPNet的待检测图像分成两部分。其中

直接连接到CSPNet的末端，而

将穿过若干个部分密集层。first through the channel

The image to be detected input to CSPNet is divided into two parts. in

is directly connected to the end of the CSPNet, while

will pass through several partially dense layers.

将经历一个部分过渡层，然后部分过渡层的输出X _T 将与

拼接并输出最终的特征图X _U 。The final output of several partially dense layers

will go through a partial transition layer, then the output X _T of the partial transition layer will be the same as

Concatenate and output the final feature map X _U .

The above formulas (1)-(3) and formulas (4)-(6) respectively represent the forward propagation formula and the back propagation formula of CSPNet.

和没有经过CSPNet的待检测图像的特征映射

都被单独拼接并生成最终的特征图，双方都不包含属于对方的用于更新权重的重复梯度信息，从而使模型避免了过多的重复梯度信息，减小了模型复杂度。From formulas (1)-(6), it can be seen that from CSPNet

and the feature map of the image to be detected without CSPNet

Both are spliced separately to generate the final feature map, and neither side contains the repeated gradient information for updating the weights belonging to the other side, so that the model avoids too much repeated gradient information and reduces the model complexity.

S32: The feature fusion network of yolov5 uses the Path Aggregation Network (PANET), which is used to further process and fuse the image information to be detected processed in the backbone layer to generate a feature pyramid. The feature pyramid enhances the model's detection of objects at different scales, so that it can recognize the same object of different sizes and scales.

The feature extraction network of PANET adopts an improved FPN (Feature Pyramid Networks) structure, which improves the propagation of low-level features in the neural network. Each stage of the pipeline takes the feature maps of the previous stage as input, and processes them with 3*3 convolutional layers, and the output is added to the same stage feature maps of the top-down pipeline through side connections. These feature maps are The next stage of processing provides information. The feature maps of different layers are fused together after adaptive pooling operation, and passed to the detector module at the end of the neural network model to generate the relevant confidence and predicted frame position information of each predicted category in the image to be detected.

The processing procedure of step S4 is as follows:

S41: The main process of training the neural network model for target detection is to encapsulate the images of the training set with a fixed size (batchsize) and format and then transfer them to the constructed neural network, obtain the prediction results of the model, and calculate the model. The loss of the output and the true value, calculate the gradient of the loss value, and finally update the model parameters with the gradient descent algorithm. The significance of introducing the loss function is to transform the abstract network training process into a mathematical optimization problem, and adjust the model parameters by finding the optimal solution of the loss function, so as to maximize the model performance.

S42: The loss function of the target detection algorithm mainly includes a positioning loss function and a classification loss function, and the mathematical formulas are expressed as:

分别代表真实框和预测框的左上角坐标与该框的长宽值。in

Represent the coordinates of the upper left corner of the real box and the predicted box and the length and width of the box, respectively.

为当前类别预测值，

为经过激活函数后得到的当前类别的概率，

则为当前类别的真实值（0或1），

为分类损失。In the above formula, N represents the total number of categories,

is the predicted value for the current category,

is the probability of the current category obtained after the activation function,

is the true value of the current category (0 or 1),

is the classification loss.

S43: In the yolov5 algorithm adopted by the present invention, the part about the positioning loss function is different from other common target detection algorithms, and the present invention adopts CIOU as the positioning loss function, as shown in the formula:

The meaning of the parameters in the formula:

IOU: The intersection of the predicted box and the ground-truth box.

V: A parameter that measures the consistency of the aspect ratio, which can also be defined as:

Compared with the traditional localization loss function, the CIOU loss function adopted in the present invention can better improve the accuracy when the predicted frame and the real frame overlap, contain and abnormal size frames, which is more beneficial to the neural network model. train.

The processing procedure of step S5 is as follows:

S51: The single-target detection algorithm represented by the yolo series is widely used in the industry and other life scenarios that focus on real-time detection because of its strong detection speed and excellent detection accuracy. In the past, the more mature yolo series algorithms were represented by yolov3, which achieved a good balance in performance and speed, and had strong stability. However, the operation of the yolov3 target detection algorithm still has high requirements on the performance of the CPU and GPU, which makes its production and deployment difficult and the cost of living applications high. At present, the latest yolov5 algorithm not only further improves the detection accuracy of the yolo algorithm, but also further improves the detection accuracy of the yolo algorithm through methods such as data enhancement and preprocessing at the input, model improvement and feature fusion, output layer improvement, loss function improvement, and NMS process improvement. It is greatly reduced, and the running speed has been greatly improved again, which can be more appropriately applied to various mobile terminals and small device applications.

S52: The backbone network of yolov5 is mainly composed of the Conv module and the CSPNet module, which are used to extract different degrees of features from the image. By controlling the width (width_multiple) and depth (depth_multiple) of the network structure, the number of Conv modules and CSPNet modules in the entire neural network can be adjusted, so that the entire model can be flexibly controlled according to the specific needs of production and life and the computing power of the current hardware. size and complexity to obtain more suitable detection accuracy and detection speed.

Considering that the detection of smoking scenes in life has low feature complexity, high real-time requirements, and extremely high deployment requirements for mobile terminals and small devices, the network width and depth are appropriately reduced, thereby greatly reducing the size and complexity of the model. Speed has been greatly improved.

S53: Conv module and CSPNet module in the backbone network of yolov5, which are mainly constructed as traditional convolution modules. The principle is to use depth convolution to process the spatial information on each feature channel, and then use point convolution to perform Feature fusion between channels. The demand for the computing power of the device is high, and there is a certain degree of waste of computing resources, which is not conducive to the application of small devices with low computing power, nor is it suitable for the real-time performance and light weight that the present invention focuses on.

Therefore, the present invention improves the Conv module and CSPNet module in the yolov5 backbone network, and introduces the Ghost idea. The core is to use normal convolution to generate some real feature maps (feature maps), and then use these real feature maps to undergo linear transformation (Cheap operations). ) to obtain the ghost feature map, and finally the complete feature layer is composed of the real feature layer and the ghost feature layer. The designed network structure is Ghost-Conv and C3-Ghost. Under the condition of ensuring the accuracy of the model, the model complexity is further reduced, the model is more lightweight, and it is more suitable for the deployment of small devices, and it is also good for large devices. running speed.

A partial network structure diagram of the yolov5 backbone network (backbone) is shown in FIG. 1 , and a partial network structure diagram of the yolov5 backbone network (backbone) according to the modified embodiment of the present invention is shown in FIG. 2 .

The processing procedure of step S6 is as follows:

S61: Import the file path of the data set processed and divided by the S1 process into the .yaml file of yolov5, run the program, read the information of the image used for training and the position information of the marked real frame, and generate the relevant information in the data set The train.cache file and the val.cache file.

S62: Set relevant parameters for training the neural network model. In the present invention, the number of training rounds of deep learning is set to 300, the batch-size is set to 24, the image-size is set to 640, and an RTX2060 graphics card is used for training;

S63: The present invention uses tensoboard for visualization in the training process, and monitors the convergence of the loss function and the improvement of the overall performance of the model in real time during the training process. When it is detected that the training process for nearly 100 rounds has not improved significantly, or the number of training rounds reaches 300 , end training and save the model file.

The processing procedure of step S7 is as follows:

S71: Use the data set divided for testing in S1 to perform model testing;

S72: Import the path of the test file into the .yaml file in the yolov5 main program, execute the val.python program, and generate the relevant performance indicators of the model;

S73: Import the trained model file into the detect.py program, enter the path of the image or video to be detected, and the model will calculate the relevant confidence levels of all categories in the current image, and obtain the final recognition frame by selecting the highest confidence level. Draw in the original image to complete the detection process;

As a whole, the above steps include:

Data set division and import;

Programming environment configuration and program implementation;

training of neural networks;

Final effect test.

Among them, the division of the data set refers to setting the marked data set as the training set and the test set respectively, and then importing it into the .yaml file of yolov5, and generating the corresponding train.cache file and val.cache file by running the program. Carry out the subsequent network training process.

The configuration and program implementation of the programming environment are performed under the windos 10 system, using pycharm + python3.9 to configure the programming environment, installing pytorch and other auxiliary packages of 1.9.1, using RTX2060 graphics card and installing cuda and cudnn of version 11.1. Program calculation acceleration.

The training of the neural network is completed by importing the dataset, constructing the network model framework, setting the number of training rounds to 300, batch-size to 24, image-size to 640, setting other hyperparameters, setting the GPU device, and executing the train.py program. training process.

The final effect test is to import the path of the test file into the .yaml file in the yolov5 main program after completing the training process of the neural network, and execute the val.python program to obtain the relevant performance results of the model. Finally, import the trained model file into the detect.py program and check the detection results.

The platform for training and testing in this experiment is Lenovo Savior R7000P, and the specific hardware configuration is NVIDIA GeForceRTX 2060 (6G), AMD Ryzen 7 4800H with Radeon Graphics.

Most of the data sets used in this experiment are obtained from the Internet. Generally, the data sets obtained are in xml format. In this case, the xml format needs to be converted into txt format using code. At the same time, the data set needs to be divided into training set and verification set. This step uses the open source code on the network to convert the format of the experimental data set and divide the training set and verification set. In the dataset, Annotations store label files in xml format, and JPEGImages store photo data files. In the data division code, the classes that have been marked in the xml should be filled in correctly. TRAIN_RATIO is used to determine the ratio of dividing the training set and the validation set. For example, when filling in 80, it means dividing 80% of the data into the training set and 20% of the data into the validation set. Running the code will generate the images and labels folders in the current directory, and the train folder and the val folder will be generated in both folders.

This experiment uses python as the compilation language and the pytorch deep learning framework. The overall structure of the project code mainly includes files such as data, models, utils, weights, detect.py, train.py, test.py, and requirements.txt. Among them, data is used to store some configuration files (yaml files), models are used to store models, utils is used to store yolov5 tool functions, weights is used to store trained weight files, and requirements.txt is used to download dependencies for running programs. .

After downloading the dependency package, first open the corresponding yaml file in the data directory, and modify the training set and verification set addresses to the data set addresses that you have made. Next, modify the model configuration file in the models directory to find the pre-training weights After opening the corresponding model configuration file, modify the category item to 1. In this example, only somking is detected. After the modification, you can start training, open train.py, change the parameters such as weights, cfg, data, etc., set the number of training rounds to 300, batch-size to 24, num_workers to 0, and use GPU to start training.

After training, take best.pt in the weights folder of the corresponding training under the run folder in the root directory as the weight file. Apply the trained model weight file to the Map generation executable file and the test executable file to get the experimental results, and compare the experimental results with the results of the network training before the improvement. Before the improvement, the size is 13.7MB, and the size after the improvement is 7.39MB, the accuracy of the model before the improvement is 0.841, and the accuracy of the model after the improvement is 0.861, thereby verifying the effectiveness of the technical solution of the present application.

Embodiment 2

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

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

The target detection module is configured to: use the lightweight neural network model to calculate the relevant confidence levels of all categories in the received current image, and obtain the final recognition frame by selecting the highest confidence level and draw it in the original image to complete the detection process.

The invention relates to neural network, deep learning, machine vision, and target detection technology, and mainly uses the latest single-stage target detection algorithm yolov5. By adjusting the width (width_multiple) and depth (depth_multiple) of the network structure, the backbone network (backbone ) in Conv and CSPNet are improved, and the complexity of the model is effectively reduced under the condition of ensuring the accuracy of the model, which not only obtains extremely fast running speed, but also the lightweight model can be easily moved in various mobile It runs smoothly on terminals and small devices to meet the actual needs of various production and life.

Those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by the computing device, so that they can be stored in a storage device. The device is executed by a computing device, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps in them are fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, they do not limit the scope of protection of the present invention. Those skilled in the art should understand that on the basis of the technical solutions of the present invention, those skilled in the art do not need to pay creative work. Various modifications or deformations that can be made are still within the protection scope of the present invention.

Claims (10)

1. An image target detection method based on a lightweight neural network model is characterized by comprising the following steps:

inputting a path of a picture or a video to be detected;

and calculating the related confidence degrees of all the classifications in the received picture to be detected by using the lightweight neural network model, selecting the highest confidence degree to obtain a final recognition frame, drawing the recognition frame in the picture to be detected, and finishing the detection process.

2. The method for detecting the image target based on the light-weight neural network model as claimed in claim 1, wherein the light-weight neural network model comprises a backbone network and a feature fusion network;

the backbone network processes the picture to be detected by utilizing convolution to generate a real characteristic layer, and then linear transformation is carried out on the real characteristic layer to obtain a phantom characteristic layer;

the feature fusion network further processes and fuses the image information to be detected processed in the phantom feature layer to generate a feature pyramid, and the feature pyramid is used for carrying out enhanced detection on objects with different scaling sizes and identifying the same object with different sizes and scales.

3. The method for detecting the image target based on the light weight neural network model as claimed in claim 1, wherein the training process of the light weight neural network model is as follows:

acquiring a data set containing target behaviors;

wherein, the data set containing the target behavior is derived from an open source data set downloaded from a network, and for the obtained unmarked data set, a marking tool is used for marking: selecting a main body which can be identified after learning and training, and then storing the position and length and width data of the main body relative to the image size in a corresponding XML file, wherein the file name corresponds to the image file;

and randomly dividing the labeled data set into a training data set and a testing data set, wherein the training data set is used for training the lightweight neural network model, and the testing data set is used for testing the lightweight neural network model.

4. The method for detecting the image target based on the light-weight neural network model as claimed in claim 3, wherein the pictures of the training data set are encapsulated by a fixed size and format and then transmitted into the constructed backbone network and the feature fusion network to obtain the prediction result of the light-weight neural network model;

calculating the loss of the output and the true value of the lightweight neural network model, calculating the gradient of the loss value, updating the lightweight neural network model parameters by using a gradient descent algorithm, and adjusting the model parameters by searching the optimal solution of the loss function.

5. The method as claimed in claim 3, wherein the labeled test data set is processed to convert an XML-formatted file into a txt-formatted file that can be read correctly, and the corresponding file name and directory in the test data set are adjusted.

6. The method for detecting the image target based on the light weight neural network model as claimed in claim 2, wherein the backbone network and the feature fusion network are used for operating yolov5 target detection algorithm.

7. The method for detecting the image target based on the light weight neural network model as claimed in claim 1, wherein the light weight neural network model is applied to real-time detection of smoking scenes in daily life.

8. The method for detecting the image target based on the light weight neural network model as claimed in claim 2, wherein each stage of the feature fusion network path takes the feature map of the previous stage as input, and the feature map is processed by a convolution layer, and the output is added to the feature map of the same stage of the top-down path through lateral connection, and the feature maps provide information for the processing of the next stage; and fusing the feature maps of different layers together after self-adaptive pooling operation, and then detecting to generate the related confidence coefficient and the position information of a prediction frame of each prediction category in the image to be detected.

9. The method as claimed in claim 2, wherein the backbone network mainly comprises a Conv module and a CSPNet module, and the number of the Conv module and the CSPNet module in the whole neural network is adjusted by controlling the width and depth of the network structure.

10. An image target detection system based on a lightweight neural network model is characterized by comprising:

a data input module configured to: inputting a path of a picture or a video to be detected;

an object detection module configured to: and calculating the related confidence degrees of all the classifications in the received current image by using the lightweight neural network model, obtaining a final recognition frame by selecting the highest confidence degree, and drawing in the original image to finish the detection process.

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