Object detection has become one of the most critical and challenging tasks in computer vision. With the rapid evolution of deep learning over the past decade, researchers have made significant advancements in object detection, particularly in the development of single-stage and two-stage detectors. Single-stage detectors streamline the detection process, making them faster, while two-stage detectors, although more complex, achieve higher accuracy. This paper presents a comprehensive review of traditional and modern object detection techniques. It explores both classical methods, such as Histograms of Oriented Gradients (HOG) and Deformable Part Models (DPM), as well as the latest deep learning-based approaches, including those leveraging convolutional neural networks (CNNs) and transformers. We also compare the performance of single-stage and two-stage detectors in terms of accuracy and inference time. Furthermore, we highlight the strengths, limitations, and future directions of these approaches in real-world applications, such as autonomous vehicles, surveillance, and robotics.