YOLO (You Only Look Once) views object detection as one end to end prediction problem. The image is divided into a grid, and each grid cell predicts bounding boxes and class probabilities at the same time. Because the whole image is processed in a single forward pass, YOLO is famously fast and ideal for real time work. SSD (Single Shot MultiBox Detector) also predicts boxes and labels in one go, but it uses several feature maps at different scales. This multi scale strategy helps SSD recognize both tiny and large objects more reliably, leading to a strong balance between speed and accuracy. YOLO (You Only Look Once) works by making grid-based predictions across the entire image, allowing it to localize and classify objects in a single pass. Each grid cell can predict multiple bounding boxes, which helps YOLO detect several objects at once, even in busy scenes. One of its biggest strengths is real-time processing the model is designed for speed, making it ideal for applications where fast decision-making matters. All of this is achieved through a simple, unified architecture, which keeps the model efficient and deployment-friendly SSD (Single Shot MultiBox Detector) performs multi-scale detection using several feature layers within the network, meaning it can recognize objects of different sizes more effectively. It relies on predefined “default” boxes with different aspect ratios and shapes, enabling it to match objects more accurately. SSD is well-known for maintaining an excellent balance between accuracy and speed, making it reliable without sacrificing performance. It is especially strong when detecting smaller or partially overlapping objects, which gives it an advantage in complex real-world scenes.