With rapid advancements in computing and artificial intelligence, Computer Vision Systems have become one of the most prominent modern applications, relying on image and video analysis to extract precise information. Deep Neural Networks (DNNs) are fundamental tools for enhancing these systems, providing a high capability to learn from complex and diverse datasets. Deep neural networks simulate the human brain, with multiple layers that progressively process data to extract essential features. This approach is used in analyzing images and videos for various applications, including face recognition, autonomous vehicles, smart surveillance, and medical diagnostics. How It Works 1. Data Collection: Gathering a large and diverse dataset of high-quality images and videos. 2. Data Preprocessing: Cleaning the data, converting images into a processable digital format, and classifying them into categories. 3. Network Design: Selecting the appropriate network type, such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN). 4. Training: Training the network on the dataset to learn patterns and recognize key features. 5. Testing and Evaluation: Testing the model on new data and measuring performance using metrics like accuracy and error rate. Practical Applications • Face recognition in security and surveillance systems. • Autonomous vehicles for obstacle and pedestrian detection. • Medical image analysis for disease diagnosis, such as tumors and fractures. • Smart monitoring and video analytics in cities and public facilities. Advantages of Using Deep Neural Networks • Ability to learn from large and complex datasets. • High accuracy in prediction and classification. • Recognition of subtle patterns difficult for humans to detect. • Improved speed and efficiency of computer vision systems. Challenges • High computational and memory requirements. • Need for massive and diverse datasets to ensure model accuracy. • Difficulty in interpreting results due to network complexity. Conclusion Using deep neural networks to enhance computer vision systems represents a critical step toward developing intelligent and efficient applications. This approach strengthens the ability to analyze images and videos accurately, supports scientific research in artificial intelligence and visual computing, and lays the foundation for advanced practical applications in education, industry, security, and medicine.