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In the field of artificial intelligence and computer vision, object detection has become a fundamental technology for enabling machines to understand and interpret visual data. Object detection involves identifying and locating objects within images or video streams by assigning labels and bounding boxes. It plays a critical role in applications such as autonomous driving, surveillance, healthcare, and assistive technologies for visually impaired individuals. Various algorithms have been developed to improve the accuracy, speed, and efficiency of object detection systems. Among the most prominent are Region-Based Convolutional Neural Networks (R-CNN), Faster R-CNN, and You Only Look Once (YOLO), each offering unique approaches to object localization and classification. This paper explores and compares these algorithms in terms of architecture, working principles, and real-world performance. The study highlights how modern deep learning techniques, especially YOLO, enable real-time object detection with high efficiency and scalability.