Real Time Object Detection Classification Using Cnn Algorithm
Keywords:
YOLO, CNN, Real-time object detection, Deep Learning, RNN.Abstract
Real-time object detection and classification have become essential in various applications such as surveillance, autonomous vehicles, and smart systems. This paper presents a robust approach for real-time object detection and classification using Convolutional Neural Networks (CNN). The proposed method leverages deep learning techniques to automatically extract spatial features from input images and accurately identify objects within dynamic environments. A pre-trained CNN model is fine-tuned and integrated with a detection framework to achieve high accuracy and low latency. The system is evaluated on standard datasets, demonstrating improved performance in terms of precision, recall, and processing speed compared to traditional methods. The results indicate that CNN-based models provide an efficient and scalable solution for real-time object detection tasks in complex scenarios.
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