This paper provides an overview of convolutional neural networks (CNNs) in computer vision, discussing their components, applications, challenges, and future directions. CNNs are widely used in tasks such as image classification, object detection, and video prediction. The paper reviews the basic components of CNNs, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, and fully connected layers. It also discusses the evolution of CNNs, from early models like AlexNet to more advanced architectures such as VGG, GoogLeNet, ResNet, and SENet. The paper highlights the importance of CNNs in computer vision, their ability to automatically learn features from data, and their effectiveness in various tasks. It also addresses challenges such as overfitting, computational complexity, and the need for efficient training methods. The paper discusses techniques to improve CNN performance, including network pruning, knowledge distilling, and tensor decomposition. It also explores future research directions, including improving model efficiency, enhancing interpretability, and addressing domain adaptation issues. The paper concludes by emphasizing the significance of CNNs in computer vision and their potential for further advancements in the field.This paper provides an overview of convolutional neural networks (CNNs) in computer vision, discussing their components, applications, challenges, and future directions. CNNs are widely used in tasks such as image classification, object detection, and video prediction. The paper reviews the basic components of CNNs, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, and fully connected layers. It also discusses the evolution of CNNs, from early models like AlexNet to more advanced architectures such as VGG, GoogLeNet, ResNet, and SENet. The paper highlights the importance of CNNs in computer vision, their ability to automatically learn features from data, and their effectiveness in various tasks. It also addresses challenges such as overfitting, computational complexity, and the need for efficient training methods. The paper discusses techniques to improve CNN performance, including network pruning, knowledge distilling, and tensor decomposition. It also explores future research directions, including improving model efficiency, enhancing interpretability, and addressing domain adaptation issues. The paper concludes by emphasizing the significance of CNNs in computer vision and their potential for further advancements in the field.