Object detection using deep learning is a widely researched area that focuses on identifying and localizing objects within images or videos using neural network architectures. Early deep learning approaches leveraged region-based convolutional neural networks (R-CNN) and its variants (Fast R-CNN, Faster R-CNN) to generate object proposals and classify them, achieving significant improvements over traditional methods. Subsequent research introduced single-shot detectors like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector), which perform detection and classification in a unified, real-time framework. More recent studies explore feature pyramid networks (FPN), attention mechanisms, transformers (DETR), and anchor-free detection methods to improve accuracy, robustness to scale variation, and detection of small or overlapping objects. Applications span autonomous driving, surveillance, robotics, medical imaging, and augmented reality, demonstrating the effectiveness of deep learning models in handling complex and large-scale detection tasks. Current research also focuses on lightweight and edge-deployable models, multi-modal detection, and integration with instance segmentation and tracking for comprehensive scene understanding.