Convolutional Neural Networks (CNN) is an important technology in deep learning and is widely used in image processing, speech recognition, natural language processing and other fields. This article will introduce the basic principles, structure and application of CNN in computer vision.
1. Basic principles of CNN
CNN is a machine learning method based on neural networks. Its basic principle is to build a three-dimensional network structure composed of multiple neurons by simulating the connection method of human brain neurons. In CNN, each neuron is connected to all neurons in the previous layer, and the weights of these connections are updated through the backpropagation algorithm.
The characteristic of CNN lies in its properties of local connections and shared weights. Local connection means that each neuron is only connected to a local area of the input data, which greatly reduces the number of parameters of the network. Shared weights mean that the same neuron uses the same weight at all locations, which allows the CNN to better capture features such as texture and shape of the image.
2. The structure of CNN
CNN mainly consists of input layer, convolution layer, pooling layer and fully connected layer.
- input layer
The input layer is responsible for feeding raw data into the CNN, usually converting images into pixel matrices or tensors.
- convolution layer
The convolutional layer is the core part of CNN and is responsible for extracting features from the input data. Each neuron in the convolutional layer is connected to a local area of the input data, and features in this area are extracted through convolution operations. The convolution operation is a special matrix multiplication operation that can multiply and accumulate the input data and the convolution kernel point by point to obtain the output data.
- Pooling layer
The pooling layer is usually located after the convolutional layer. Its function is to reduce the dimension of the output data and reduce the amount of calculation. Each neuron in the pooling layer is responsible for aggregating a local area of the input data, such as maximum pooling and average pooling.
- Fully connected layer
The fully connected layer is usually located at the last few layers of CNN. Its function is to integrate and classify the output data of the previous layers. Each neuron in the fully connected layer is connected to all previous neurons, and the previous output data are weighted and summed through fully connected operations to obtain the final classification result.
3. Application of CNN in computer vision
Computer vision is one of the most widely used fields of CNN, including image classification, target detection, face recognition, etc. For example, in image classification, CNN can automatically extract features in images by training and learning on a large number of images, thereby achieving automatic classification of images. In target detection, CNN can automatically detect and locate target objects in images by scanning and classifying images through sliding windows.
4. Summary
CNN is an important technology in deep learning. Its characteristics of local connections and shared weights make CNN have broad application prospects in image processing, speech recognition, natural language processing and other fields. With the continuous development of deep learning technology, CNN will be more widely used and developed in the future.