Object detection is a task in computer vision. It focuses on finding and locating objects in images or videos. With deep learning advancements, object detection systems have improved a lot in accuracy and speed. This research paper presents an object detection system using modern deep learning algorithms. The system uses neural networks (CNN) to detect and classify objects in real-time. A large labeled dataset trains the model to recognize object categories. The system processes images. Generates bounding boxes around detected objects along with their labels. Object detection systems use frameworks like YOLO to enhance detection speed and performance. The proposed method aims to achieve precision while maintaining low computational complexity. Experimental results show that the system performs under different lighting and environmental conditions. The model shows accuracy in detecting multiple objects simultaneously .Object detection systems can be integrated into applications like surveillance systems and autonomous vehicles. They can also be used in security systems and traffic monitoring. The research highlights the importance of deep learning techniques in object detection systems.The proposed approach provides an scalable solution for real-world applications. Future work will focus on improving detection accuracy and expanding the dataset. The system can be optimized for embedded devices. Overall the proposed object detection system demonstrates promising results, for visual recognition tasks.
Object Detection, Computer Vision, Deep Learning, Convolutional Neural Network (CNN) YOLO Algorithm, Image Processing, Real-Time Detection, Machine Learning, Artificial Intelligence, Bounding Box Detection
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