This paper provides an introduction to Convolutional Neural Networks (CNNs), a specialized type of Artificial Neural Network (ANN) designed for image recognition tasks. CNNs are inspired by biological neural systems and are characterized by their ability to learn and optimize through self-organization. The authors discuss the fundamental concepts of ANNs, including supervised and unsupervised learning, and highlight the challenges of traditional ANNs in handling complex image data due to computational complexity and overfitting issues. The paper outlines the architecture of CNNs, which consists of convolutional layers, pooling layers, and fully-connected layers. Convolutional layers use learnable kernels to extract features from the input image, while pooling layers reduce the spatial dimensions of the activation maps to decrease computational complexity. Fully-connected layers then perform classification based on the learned features. Key techniques such as parameter sharing and zero-padding are discussed to optimize the model's performance and reduce overfitting. The authors also provide guidelines for designing CNN architectures, including the use of stacked convolutional layers and the importance of proper hyperparameter settings. The paper concludes by emphasizing the power and simplicity of CNNs in image analysis tasks, aiming to make the field more accessible to beginners. It references several key papers and studies that have contributed to the development and application of CNNs in various fields, such as object detection and pedestrian detection.This paper provides an introduction to Convolutional Neural Networks (CNNs), a specialized type of Artificial Neural Network (ANN) designed for image recognition tasks. CNNs are inspired by biological neural systems and are characterized by their ability to learn and optimize through self-organization. The authors discuss the fundamental concepts of ANNs, including supervised and unsupervised learning, and highlight the challenges of traditional ANNs in handling complex image data due to computational complexity and overfitting issues. The paper outlines the architecture of CNNs, which consists of convolutional layers, pooling layers, and fully-connected layers. Convolutional layers use learnable kernels to extract features from the input image, while pooling layers reduce the spatial dimensions of the activation maps to decrease computational complexity. Fully-connected layers then perform classification based on the learned features. Key techniques such as parameter sharing and zero-padding are discussed to optimize the model's performance and reduce overfitting. The authors also provide guidelines for designing CNN architectures, including the use of stacked convolutional layers and the importance of proper hyperparameter settings. The paper concludes by emphasizing the power and simplicity of CNNs in image analysis tasks, aiming to make the field more accessible to beginners. It references several key papers and studies that have contributed to the development and application of CNNs in various fields, such as object detection and pedestrian detection.