Object detection systems are a key component of computer vision that allow machines to recognize and locate objects within an image or video. These systems use advanced machine learning models, such as convolutional neural networks (CNNs), to detect multiple objects, identify their types, and provide precise coordinates of their locations. Object detection has a wide range of applications, including autonomous vehicles, robotics, video surveillance, retail analytics, and medical imaging. In the automotive industry, for example, object detection is critical for enabling features like collision avoidance and self-driving technology. In retail, it helps track customer behavior, optimize product placement, and enhance inventory management. Object detection models are trained on large datasets, improving their ability to accurately identify objects in diverse environments. The integration of cloud computing further enhances the scalability of these systems, allowing real-time processing of large video streams. As the technology evolves, object detection systems will continue to play a crucial role in enhancing automation, safety, and efficiency across industries.