Child Protection: Automatic Television Channel Switching System Using Artificial Intelligence
Introduction
In today's modern world, television has become one of the primary sources of information and entertainment for people of all ages. However, despite the many benefits that television offers to its users, the presence of inappropriate and harmful content can be a significant concern, especially for children and adolescents. To protect this group of society and prevent their access to unsuitable content, the present project is based on artificial intelligence and image processing
:Image Processing and Deep Neural Networks
In this project, image processing techniques and deep neural networks are utilized to detect inappropriate images. To create an efficient detection system, models based on deep neural networks, such as Convolutional Neural Networks (CNNs), are employed. These models are designed and trained as a fundamental part of this project
:Neural Network Architecture
To create a model for image detection, a deep convolutional neural network architecture is used. These networks connect convolutional layers, sequential layers, and fully connected layers. For instance, architectures like VGG16 or ResNet can be employed. These networks, by adding deep and complex layers, gain the capability to recognize intricate patterns in images
:Training the Network
One of the primary challenges in training the image detection model is adjusting various network variables and training it with high accuracy and efficiency. For this purpose, we make use of a dataset that contains both inappropriate and appropriate images, which must be correctly processed and categorized into suitable classes
:Challenges and Improvements
During the network training process, we may face challenges such as overfitting and gradient vanishing. To address these challenges, techniques like Dropout and Batch Normalization can be employed. Additionally, various optimization settings can enhance the network's performance