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This article will explain in detail what is the difference between big data image classification and target detection technology. The content of the article is of high quality, so the editor will share it with you for reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

Image classification and target detection are important research methods in the field of computer vision. These technologies help machines understand and recognize real-time objects and environments, and help digital images as input. Over the years, computer vision technology has been used in many fields, including health care, manufacturing, retail and so on.

Because methods such as image classification and target detection focus on target recognition in digital images, there is often confusion: what are these two technologies and how are they different?

We will learn what these two technologies are and discuss the various scenarios that make them different.

Image classification

To put it simply, image classification is a technique used to classify or predict the categories of specific objects in the image. the main purpose of this technology is to accurately identify the features in the image.

The working principle of image classification

Generally speaking, image classification technology can be divided into parametric and non-parametric, supervised and unsupervised, hard classifier and soft classifier. For supervised classification, the technique transmits the results based on the established decision boundary, which mainly depends on the input and output provided by the training model. However, in the case of unsupervised classification, the technique provides results based on the analysis of the input data set itself; features are not directly input into the model.

The main steps involved in image classification technology are to determine the appropriate classification system, feature extraction, selection of good training samples, image preprocessing and selection of appropriate classification methods, classification post-processing, and finally evaluate the overall accuracy. In this technique, the input is usually an image of a specific object, such as the rabbit in the image above, and the output is a prediction class that defines and matches the input object. Convolution neural network (CNNs) is the most commonly used neural network model for image classification.

Types of image classification technology

Supervised image classification techniques include parallel hexahedron technology, minimum distance classifier, maximum likelihood classifier and so on. In a research paper, the researchers mentioned the following types of image classification techniques

● Image Classification based on different Sensor Information

● Image Classification based on the Properties of training samples

● image classification based on various parameters used in the data

● Image Classification based on the property of data Pixel Information

● image classification based on the number of outputs generated by each spatial data element

● image classification based on spatial information properties.

Shortcoming

The disadvantage of ● in supervised and unsupervised image classification technology is that the training phase requires a lot of time, so it is not suitable to deal with big data.

Target detection

The problem definition of target detection is to determine the position of the target in a given image, such as target location, and which category each target belongs to, that is, target classification. To put it simply, target detection is an image classification technology, in addition to classification, this technology can also identify the location of target instances from a large number of predefined categories in natural images.

This technology can search for specific kinds of objects, such as cars, people, animals, birds and so on, and has been successfully applied to the next generation of image and video processing systems. Only with the advent of deep learning methods will the latest advances in this technology become possible.

Target detection technology can be applied to practical engineering, such as face detection, pedestrian detection, vehicle detection, traffic sign detection, video surveillance and so on.

The working principle of Target Detection

The traditional target detection model is mainly divided into three stages: information region selection, feature extraction and classification. There are several popular target detection models based on deep learning, which have been used by organizations and academia to achieve efficient and accurate results of target detection from images. Popular models include MobileNet, You Only Live One (YOLO), Mark RCNN, RetinaNet and so on.

Shortcoming

In recent years, the problem of target detection in controlled environment has achieved great success. However, this problem is still not solved in uncontrolled places, especially when the object is placed in an arbitrary position in a cluttered and occlusive environment.

On big data image classification and target detection technology what is the difference is shared here, I hope the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

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