How to write an artificial intelligence-based target detection system using Java

With the increasing development of artificial intelligence technology, target detection systems play an extremely important role in the field of computer vision. This kind of system can automatically identify key objects from images or videos to track and analyze targets. In this article, we will introduce how to write an artificial intelligence-based object detection system using Java.

1. Install and configure Java and OpenCV

First, we need to install Java and OpenCV. Java is a programming language for writing target detection systems using Java, and OpenCV is a computer vision library that provides many practical APIs and tools to process and analyze images. We can install and configure Java and OpenCV through the following steps:

1.1 Install Java JDK

Java JDK can be downloaded from the official website. After the installation is complete, you need to set the JAVA_HOME and PATH environment variables. In Windows systems, you can add the following content to the environment variables:

Variable name: JAVA_HOME

Variable value: C:Program FilesJavajdk1.8.0_45

Variable name: PATH

Variable value: %JAVA_HOME% in

1.2 Install OpenCV

OpenCV can be downloaded from the official website. Once the download is complete, unzip the file and copy it to your project's working directory. Then copy the opencv-3.4.3.jar file in the folder to the lib folder of the project. Finally, add the third-party local library folder opencv uildjavad (under Windows) or opencv uildjavalib (under Linux) in the folder to the system's path environment variable.

2. Load images and process them

After we have completed the installation and configuration of Java and OpenCV, we can start building the target detection system. First, we need to load the image and perform some basic operations. We can use the following code to achieve these operations:

public static void main(String[] args) {

   // 载入图像
   Mat image = Imgcodecs.imread("test.jpg");

   // 缩放图像
   Size size = new Size(800,600);
   Imgproc.resize(image,image,size);

   // 转换为灰度图像
   Mat grayImage = new Mat();
   Imgproc.cvtColor(image,grayImage,Imgproc.COLOR_BGR2GRAY);

   // 模糊处理
   Imgproc.GaussianBlur(grayImage,grayImage,new Size(3,3),0);

   // 边缘检测
   Mat edges = new Mat();
   Imgproc.Canny(grayImage,edges,50,150);

   // 显示图像
   HighGui.imshow("Test",edges);
   HighGui.waitKey(0);
   System.exit(0);
}

In this code, we first use the Imgcodecs.imread() function to load the image from the local file system. Then, we use the Imgproc.resize() function to scale the image to 800×600 dimensions. Next, we convert the image to grayscale using the Imgproc.cvtColor() function. Then, we use the Imgproc.GaussianBlur() function to blur the grayscale image to reduce noise and interference. Finally, we use the Imgproc.Canny() function for edge detection for subsequent target detection.

3. Target detection

After basic image processing, we can start target detection. In order to achieve this function, we need to first choose a suitable target detection algorithm. In this article, we will use an OpenCV-based Haar feature classifier for object detection. The specific implementation steps are as follows:

3.1 Choose the appropriate Haar classifier

Haar classifier is a classic target detection algorithm. Its basic idea is to identify objects by observing their morphological characteristics. . Specifically, the Haar classifier uses an internal feature value to represent the target, which is obtained by calculating the difference in gray value within the target area. If the morphological characteristics of the target object can be represented by a set of feature values, then we can determine whether the target object exists in an image by comparing these feature values.

In this article, we will use the trained Haar feature classifier provided by OpenCV for target detection. These classifiers already contain a large number of positive and negative samples and can be trained through backpropagation.

3.2 Training Haar classifier

In order to start artificial intelligence target detection, we need to use the built-in training tool of OpenCV to train the Haar classifier. The training process requires a set of positive and negative sample images. Usually, the more sample images, the better the effect.

3.3 Target detection

Through the above steps, we have completed the training of the Haar classifier and can use it for target detection. In Java, we can use the following code to implement the target detection of the Haar classifier:

public static void main(String[] args) {

   // 载入图像
   Mat image = Imgcodecs.imread("test.jpg");

   // 装载分类器
   CascadeClassifier detector = new CascadeClassifier("classifier.xml");
   MatOfRect targets = new MatOfRect();

   // 检测目标
   detector.detectMultiScale(image,targets);

   // 在图像上标示目标
   for(Rect rect: targets.toArray()){
      Imgproc.rectangle(image,rect.tl(),rect.br(),new Scalar(0,0,255),2);
   }

   // 显示图像
   HighGui.imshow("Test",image);
   HighGui.waitKey(0);
   System.exit(0);
}

In this code, we first use the Imgcodecs.imread() function to load the image from the local file system. We then build a classifier object using the CascadeClassifier class and use it to detect the image. The detection results are stored in an object of type MatOfRect. Finally, we use the Imgproc.rectangle() function to mark the target on the image, and use the HighGui.imshow() function to display the detection results.

4. Summary

In this article, we introduced how to write an artificial intelligence-based target detection system using Java. We first introduced the installation and configuration of Java and OpenCV, then demonstrated some basic image processing functions, and finally implemented object detection using the Haar feature classifier. With this foundation of knowledge and skills, readers can further learn and explore more in-depth and advanced applications and technologies in target detection systems, such as YOLO, RCNN, etc., in order to better adapt to the evolving artificial intelligence era.

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