SoFunction
Updated on 2025-03-02

Springboot integrates Java DL4J to realize the entire process of traffic sign recognition system

Preface

In today's era of rapid development of technology, autonomous driving technology has become a popular research field. Traffic sign recognition is one of the key links in the autonomous driving system. It can help cars accurately understand road conditions and abide by traffic rules. This article will introduce how to use Spring Boot to integrate Java Deeplearning4j to build a traffic sign recognition system.

1. Technical Overview

1. Neural Network Selection

In this traffic sign recognition system, we choose to use a convolutional neural network (Convolutional Neural Network，CNN）。CNNIt has excellent performance in the field of image recognition, the main reasons are as follows:

  • Local connection: Neurons in CNN are connected only to local areas of the input image, which allows the network to capture local features in the image, such as edges, textures, etc. For objects such as traffic signs with specific shapes and color characteristics, local connections can effectively extract key information.
  • Weight sharing: The filters in CNN share weights across the entire image, which greatly reduces the number of parameters, reduces the complexity of the model, and also improves the generalization ability of the model.
  • Hierarchy: CNN is usually composed of multiple convolutional layers, pooling layers and fully connected layers. This hierarchy can gradually extract advanced features of the image, thereby achieving accurate recognition of complex images.

2. Dataset format

The traffic sign datasets we use usually contain the following formats:

  • Image File: The data set consists of a large number of traffic sign images, and the image format can be common JPEG, PNG, etc. Each image file represents a traffic sign.
  • Tag file: The label file corresponding to the image file, used to identify the traffic sign category represented by each image. The label can be a digital encoding or a text description.

Here is an example of a simple dataset directory structure:

traffic_sign_dataset/
├── images/
│   ├── 
│   ├── 
│   ├──...
├── labels/
│   ├── 
│   ├── 
│   ├──...

In a tag file, digital encoding can be used to represent different traffic sign categories, such as:0Indicates the speed limit sign,1Indicates a prohibition sign,2Indicates indication signs, etc.

3. Technology stack

  • Spring Boot: An open source framework for building enterprise-grade applications that provide fast development, automatic configuration, and easy deployment.
  • Java Deeplearning4j: A Java-based deep learning library that supports a variety of neural network architectures, includingCNN, recurrent neural network (Recurrent Neural Network，RNN)wait. It provides an efficient computing engine and rich tools to facilitate developers to develop deep learning applications.

2. Maven dependency

In the project's file, you need to add the following Maven dependencies:

<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-nn</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-ui</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId></groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

These dependencies will be introducedDeeplearning4jandSpring BootRelated libraries so that we can use them for traffic sign recognition in our projects.

3. Code examples

1. Data loading and preprocessing

First, we need to load the traffic sign dataset and preprocess it. Here is a sample code:

import ;
import org.;
import org.;
import org.;
import org.;
import org.;

import ;
import ;
import ;

public class DataLoader {

    public static ListDataSetIterator loadData(String dataDirectory) {
        // Load image file        File imageDirectory = new File(dataDirectory + "/images");
        NativeImageLoader imageLoader = new NativeImageLoader(32, 32, 3);
        List&lt;INDArray&gt; images = new ArrayList&lt;&gt;();
        for (File imageFile : ()) {
            INDArray image = (imageFile);
            (image);
        }

        // Load the tag file        File labelDirectory = new File(dataDirectory + "/labels");
        List&lt;Integer&gt; labels = new ArrayList&lt;&gt;();
        for (File labelFile : ()) {
            // Assume that there is only one number per line in the label file, indicating the label category            int label = ((labelFile));
            (label);
        }

        // Create a dataset        DataSet dataSet = new DataSet((new INDArray[0]), ().mapToDouble(i -&gt; i).toArray());

        // Data normalization        DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
        (dataSet);
        (dataSet);

        return new ListDataSetIterator(dataSet, 32);
    }
}

In this example, we useNativeImageLoaderLoad the image file and convert it toINDArrayFormat. Then we read the tag file and get the tag category for each image. Finally, we create aDataSetobject, and useImagePreProcessingScalerPerform data normalization.

2. Model construction and training

Next, we build a convolutional neural network model and train it using the loaded data. Here is a sample code:

import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;

public class TrafficSignRecognitionModel {

    public static MultiLayerNetwork buildModel() {
         builder = new ()
               .seed(12345)
               .weightInit()
               .updater(org.)
               .l2(0.0005)
               .list();

        // Add convolutional layer        (0, new (5, 5)
               .nIn(3)
               .stride(1, 1)
               .nOut(32)
               .activation()
               .convolutionMode()
               .build());

        // Add pooling layer        (1, new org.(org.)
               .kernelSize(2, 2)
               .stride(2, 2)
               .build());

        // Add more convolutional and pooling layers        (2, new (5, 5)
               .nOut(64)
               .activation()
               .convolutionMode()
               .build());
        (3, new org.(org.)
               .kernelSize(2, 2)
               .stride(2, 2)
               .build());

        // Add full connection layer        (4, new ()
               .nOut(1024)
               .activation()
               .build());

        // Add output layer        (5, new ()
               .nOut(10) // Assume there are 10 types of traffic signs               .activation()
               .build());

        return new MultiLayerNetwork(());
    }

    public static void trainModel(MultiLayerNetwork model, ListDataSetIterator iterator) {
        ();
        for (int epoch = 0; epoch &lt; 10; epoch++) {
            (iterator);
            ();
        }
    }
}

In this example, we useConstruct a convolutional neural network model. The model contains multiple convolutional layers, pooling layers, fully connected layers, and output layers. We useInitialize the weight and set some hyperparameters, such as learning rate, regularization coefficient, etc. Then, we useMultiLayerNetworkoffitMethod trains the model.

3. Prediction and results display

Finally, we can use the trained model to predict the new traffic sign image and show the results. Here is a sample code:

import org.;
import org.;
import org.;
import org.;
import org..Nd4j;

import ;

public class Prediction {

    public static int predict(MultiLayerNetwork model, File imageFile) {
        // Load the image and preprocess it        NativeImageLoader imageLoader = new NativeImageLoader(32, 32, 3);
        INDArray image = (imageFile);
        DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
        (image);

        // Make predictions        INDArray output = (image);
        return (output, 1).getInt(0);
    }
}

In this example, we useNativeImageLoaderLoad new traffic sign images and perform data normalization. We then use the trained model to predict the image, returning the predicted label category.

IV. Unit Test

To ensure the correctness of the code, we can write some unit tests. Here is an example of test data loading and model training:

import org.;
import org.;
import ;
import ;

import static ;

public class TrafficSignRecognitionTest {

    private MultiLayerNetwork model;

    @BeforeEach
    public void setup() {
        model = ();
    }

    @Test
    public void testLoadData() {
        String dataDirectory = "path/to/your/dataset";
        ListDataSetIterator iterator = (dataDirectory);
        assertNotNull(iterator);
    }

    @Test
    public void testTrainModel() {
        String dataDirectory = "path/to/your/dataset";
        ListDataSetIterator iterator = (dataDirectory);
        (model, iterator);
        assertNotNull(model);
    }
}

In this test, we first build a model, and then test the methods of data loading and model training. We useassertNotNullAssert to ensure that the results of data loading and model training are not empty.

V. Expected output

When we run the traffic sign recognition system, the expected output is to accurately classify the input traffic sign images. For example, if an image of a speed limit sign is entered, the system should output the corresponding tag category, such as "speed limit sign".

6. References

  • Deeplearning4j official documentation
  • Spring Boot Official Documentation
  • "Deep Learning" (by Ian Goodfellow, Yoshua Bengio, Aaron Courville)

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